Science topic

Climatology - Science topic

Climatology is the study of climate, scientifically defined as weather conditions averaged over a period of time, and is a branch of the atmospheric sciences.
Questions related to Climatology
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I will use climatological models in my research work but I have not yet mastered any programming language, now I would like to have advice on the choices to start.
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Hi David,
When you are working on paleoclimatology, there is a requirement for huge datasets, hence you will need robust calculation and efficiency. Moreover, it will also depend upon which datasets one is using. In general, MATLAB and R can work, though R is open source. Here is the link for seeing Datasets and Language used for analysis:
Cheers
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DIfferent Mathematical Techniques are being used for regionalization. For example in different references, the authors regionalize the area across the country under different climates.
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Nice question
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Hello all,
I would like to perform a hydrological analysis using RCM (CORDEX) data that need to be downscaled and bias corrected prior to application. I need an advise on which tool I could use to perform this.
Thank you
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Hi All,
CMhyd is handy for bias correction of GCMs and RCMs.
Simple and effective. Can be applied for both netCDF and ASCII format.
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I downloaded Cordex historical data from Copernicus CDS (https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-single-levels?tab=form). Data are in NetCDF format. When I read the files using a NetCDF reader, I see that they have a Lambert Conformal Conic projection type (screenshot attached). I need to extract weather data for specific locations using regular coordinates. Could someone please tell me how to deal with these NetCDF files so I can be able to extract my data?
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Thank you Mr. Reuben Reyes
But to use GDAL you need to install Phyton, right?
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I am analyzing seasonal climate forecast skill (netCDF data) for a region comprised of the U.S. states of Texas, Oklahoma, Kansas, and Colorado using NCL (NCAR Command Language). Can I use shapefiles of these states to create a spatial subset of the data so it covers only these states, and if so, how? At the moment I use latitudes and longitudes to define my study region?(see the example plot attached), but some of the most extreme model outliers are outside my study region (for example, Mexico, Missouri, Iowa), which distorts my results. Can anyone help?
Thank you very much!
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This can be done in R using the raster package.
# R Code ---------------------------------------------------------------------------------------------------------------
library(raster)
# Read in shapefile and netCDF
shp = shapefile('C:/path/to/shapefile/ILshapefile.shp')
br = brick('C:/path/to/netcdf/hurs_bced_1960_1999_gfdl-esm2m_hist_1950-2004.nc4')
# Transform shapefile to match the CRS of the netCDF
shp = spTransform(shp, crs(br))
# Mask all cells outside of shapefile
br.subset = mask(br, shp)
# See the result
plot(br.subset, 1)
plot(shp, add=T)
##copied from Jacob Fraser
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Our group is trying to investigate the relationship between regional precipitation (especially over regions of east Asia monsoon and South Asia Monsoon) climatology and atmospheric front? We aim to analyze plenty of front cases to show the physical mechanisms and the climatological changes in the cyclone and front-induced precipitation.
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You should read the book (Today's Meteorology: An Introduction to Weather, Climate and Environment), Fronts Section
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Hello all,
I want to know how to create MONTHLY gridded data set based on point-based observational data (synoptic stations) for the rainfall and temperature.
I was read some article about creating DAILY gridded data set based on point-based observational data (synoptic stations), is this the same? or MONTHLY make a difference?
Thanks
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PRISM is a code name for a program under which the United States National Security Agency (NSA) collects internet communications from various U.S. internet? companies
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In your opinion, what is the most reliable drought index (e.g. PDSI, SPI, SPEI, etc.) for climatological and hydrological studies and why?
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Dear Dr. Alireza Araghi
Shortly, the SPI, and some other spectral drought indices can perform very good studies on drought assessing and mapping. You could see please in the following articles what we have done in Iraq to assess and map the drought.
Good luck and best regards.
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Assessing weather effects on construction project durations in kampala especially residential storey buildings! So I require assistance in obtaining climatological Data for the last 10 years and 3construction project records in Kampala!!
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Dear Abdirizak Tahir,
Recently, after refining the historical rainfall estimates over the African Continent, the US NOAA have made available the African Rainfall Climate version 2.0 (ARC2) of daily rainfall estimates since 1983 at high resolution: you may search the below mentioned link for more detail.
Cheers!
Vaid, B. H.
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We are trying to develop a forecast system for oil spills based on ocean currents climatology. This is not a real-time modeling system, but rather based on scenarios. I am interested in having information about real time forecast system and early warning alerts during oil spills, an particularly hearing experiences of actual users during contingencies.
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We at INCOIS an office under Ministry of Earth Science in India is providing this service on operational basis. The location of oil spill will be reported to us by coast guards and our oil spill model is run and the possible dispersion is reported. Each such scenario is generated using the model and the output is made available on the website. The link for looking at the oil spill trajectory prediction can be obtained from https://www.incois.gov.in/portal/osf/oosa.jsp
For more information about the same you may contact Dr SJ Prasad of INCOIS. Hen can be reached at prasadsj@incois.gov.in.
uday
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Nafiseh Pegahfar Thank you very much.
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Dear R. K. Jaiswal
?Hope you are doing well I am? Dr Babikir, Agrometoeorlogist
?Based on your published paper entitled "Statistical Analysis for Change Detection and Trend Assessment in Climatological Parameters", I am working on rainfall trend and variability analysis using excel sheet but I fount some problems with homogeneity analysis because? I don't have software for analysis.
would you plz check my analysis in attached excel sheet? and if there is any problem.
?I hope to hear postive and quick responses
?? Regards?
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Thank you very much for excellent paper entitled " Absolute homogeneity assessment of precipitation time series in an arid region of Pakistan " I follows it for calculation of homogeneity test. Would you check my calculation in the attached excel sheet if there is any mistake you can correct it
Regards
Dr Babikir
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In this part in addition to new subjects, I have highlighted some of responses from previous sections for further discussion. Please leave you comments to support/weaken any of the following statements:
1) @Harry ten Brink recapitulated a summary of a proof that CO2 is such an important Greenhouse component/gas. Here is a summary of this argument:
"a) Satellites' instruments measure the radiation coming up from the Earth and Atmosphere.
b) The emission of CO2 at the maximum of the terrestrial radiation at 15 micrometer.
b1. The low amount of this radiation emitted upwards: means that "back-radiation" towards the Earth is high.
b2. Else said the emission is from a high altitude in the atmosphere and with more CO2 the emission is from an even higher altitude where it is cooler. That means that the emission upwards is less. This is called in meteorology a "forcing", because it implies that less radiation /energy is emitted back into space compared to the energy coming in from the sun.
The atmosphere warms so the energy out becomes equals the solar radiation coming in. Summary of the Greenhouse Effect."
At first glance, this reasoning seems plausible. Nevertheless, it is based on these assumptions that the contribution of CO2 is not negligible and any other gas like N2O and Ozone has minor effect. The structure of this argument is supported by an article by Schmidt et al., 2010:
By using the Goddard Institute for Space Studies (GISS) ModelE radiation module, the authors claim that "water vapor is the dominant contributor (~50% of the effect), followed by clouds (~25%) and then CO2 with ~20%. All other absorbers play only minor roles. In a doubled CO2 scenario, this allocation is essentially unchanged, even though the magnitude of the total greenhouse effect is significantly larger than the initial radiative forcing, underscoring the importance of feedbacks from water vapour and clouds to climate sensitivity."
The following notions probably will shed light on the aforementioned argument for better understanding the premises:
Q1) Is there any observational data to support the overall upward/downward IR radiation because of CO2?
Q2) How can we separate practically the contribution of water vapor from anthropogenic CO2?
Q3) What are the deficiencies of the (GISS) ModelE radiation module, if any?
Q4) Some facts, causes, data, etc relevant to this argument, which presented by NASA, strongly support this argument (see: https://climate.nasa.gov/evidence/)
Q5) Stebbins et al, (1994) showed that there exists "A STRONG INFRARED RADIATION FROM MOLECULAR NITROGEN IN THE NIGHT SKY" (thanks to @Brendan Godwin for mentioning about this paper). As more than 78% of the dry air contains nitrogen, so the contribution of this element is not negligible.
2) The mean global temperature is not a good diagnostic to study the sensitivity to global forcing. Because given a change in this mean value, it is almost impossible to attribute it to global forcing. Zonal and meridional distribution of heat flux and temperature are not uniform on the earth, so the mean temperature value is misleading.
3) "The IPCC model outputs show that the equilibrium response of mean temperature to a doubling of CO2 is about 3C while for the other observational approaches this value is less than 1C." (R. Lindzen)
4) What is the role of the thermohaline circulation (THC) in global warming (or the other way around)? It is known that during Heinrich events and Dansgaard‐Oeschger (DO) millennial oscillations, the climate was subject to a number of rapid cooling and warming with a rate much more than what we see in recent decades. In the literature, these events were most probably associated with north-south shifts in convection location of the THC. The formation speed of North Atlantic Deep Water (NADW) affects northerly advection velocity of the warm subtropical waters that would normally heat/cool the atmosphere of Greenland and western Europe.
I really appreciate all the researchers who have participated in these discussions with their useful remarks, particularly Harry ten Brink, Filippo Maria Denaro, Tapan K. Sengupta, Jonathan David Sands, John Joseph Geibel, Ale? Kralj, Brendan Godwin, Ahmed Abdelhameed, Jorge Morales Pedraza, Amarildo de Oliveira Ferraz, Dimitris Poulos, William Sokeland, John M Wheeldon, Joseph Tham, and Boris Winterhalter.
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Link to the discussions of Global Warming (Part 1):
Link to the discussions of Global Warming (Part 2):
Link to the discussions of Global Warming (Part 3):
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Today, activity surrounding the term global warming is political not scientific. For a number of reasons internet searches yield a ratio of 10 or 20 fake science sites to one scientific one. You have to read them all. I suggest several thoughts based on what I have read.
Studies of CO2 concentrations based on the content of glaciers claim that during the time, 600 million years ago, when earth had it's biggest explosion of life specie types, CO2 concentration was 16 times what it is today. Earth was very lush in greenery. It is likely that the dinasour could not thrive in our atmosphere today because of our low CO2 concentration.
CO2 concentration is known to vary significantly in cycles of thousands of years.
Mankind, of course, does affect CO2 concentration as does the fruit fly and the rats in Baltimore. I am reminded of the classic joke about the flea crawing up the elephant's leg with sex on it's mind!
I urge you to keep an open mind and stick to science where most of us here live.
To those of you who still believe in catestrophic death of the planet by mankind, why are you not concentrating on reducing the population explosion, a much more serious threat?
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What does Statistical Analysis / Method do with Economics Faculty? Why do economics faculty only deal with statistics software or approach?
I find only Statistically implementation in Economics publication nothing more. Why not they explore other things?
What do they try to prove using statistical methods?
Are they predicting rainfall or predicting the crop in the agriculture?
This is taken care by Scientist or Climatology, what is economics faculty have do with this?
I invite all for their valuable opinions..
Thanks
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statistical analysis is the data analysis technique
this analysis can do by all the department for their data analysis
so all must use this analysis in their own way
eg
population data for economic
climate change parameter analysis for science
water quality analysis for environment
oil spill pie diagram
and so many
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The mixed layer depth (MLD) in the upper ocean is an important physical parameter for describing and studying the marine ecosystem. the question is what are the factors who influence the climatological mixed layer depth?
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Hi Ismail,
There are many factors that influence like wind, fluxes, currents, remote forcing variability of temperature, salinity , (E - P) and a combination of them. Also this varies based on the season and region too. If the regions experience high winds during a specific period it tends to deepen the MLD. If there is net heat loss then it results in convection which also deepens the MLD. Some time advection of different types of waters (high temp, low saline vice versa) can also have its influence on MLD at a region. Some times (E - P) also plays a role.
Most importantly the region of the study need to be well understood for the factors that can have influence on them and go for a deeper understanding on how the cause variability on MLD.
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I would greatly appreciate having feedback from scientists/groups specifically dedicated to the climatology of small oceanic islands worldwide (Including issues related with? as climate change, water, etc), having? information about their work, publications, etc.
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VACCIN -- is the short form of a project led by CSIR ---- just give a search and you will get it
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If i have for example humidity, is that possible to used only the humidity of different places/station as inputs in the same country and predict same humidity as the target for another place with the country.
Reference paper will highly be appreciated for the general hydro-climatological variable or any case study.
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@Sani Isah Abba,
In addition to the insightful responses of the colleagues, please take a look at the attached article.
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Hi everyone
I'm looking for a quick and reliable way to estimate my missing climatological data. My data is daily and more than 40 years. These data include the minimum and maximum temperature, precipitation, sunshine hours, relative humidity and wind speed. My main problem is the sunshine hours data that has a lot of defects. These defects are diffuse in time series. Sometimes it encompasses several months and even a few years. The number of stations I work on is 18. Given the fact that my data is daily, the number of missing data is high. So I need to estimate missing data before starting work. Your comments and experiences can be very helpful.
Thank you so much for advising me.
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Hi,
I don't know if this method work for you or not? I tell you anyway. Personally in some cases I used curve fitting methods. There are discontinued points in every data sheet. By various curve fitting methods available throughout the internet you can fix this issue.
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Hi,
Except synoptic stations, does anybody know any website/software that gives the climatological data of unequipped places?
I am aware of using interpolation methods, but, I am looking for a method that extract data for a desired location numerically (like an excel format etc,.).
Thanks,
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Good link @Zerouali Bilel. Thanks for sharing
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In this part other specific well-known subjects are revisited. Please support or refute the following arguments in a scientific manner.
1) Still there is no convincing theorem, with a low range of uncertainty, to calculate the response of climate system in terms of the averaged global surface temperature anomalies with respect to the total feedback factors and greenhouse gases changes. In the classical formula applied in the models a small variation in positive feedbacks leads to a considerable changes in the response (temperature anomaly) while a big variation in negative feedbacks causes just small variations in the response.
2) NASA satellite data from the years 2000 through 2011 indicate the Earth's atmosphere is allowing far more heat to be emitted into space than computer models have predicted (i.e. Spencer and Braswell, 2011, DOI: 10.3390/rs3081603). Based on this research "the response of the climate system to an imposed radiative imbalance remains the largest source of uncertainty. It is concluded that atmospheric feedback diagnosis of the climate system remains an unsolved problem, due primarily to the inability to distinguish between radiative forcing and radiative feedback in satellite radiative budget observations." So the contribution of greenhouse gases to global warming is exaggerated in the models used by the U.N.’s Intergovernmental Panel on Climate Change (IPCC).
3) Ocean Acidification
Ocean acidification is one of the consequences of CO2 absorption in the water and a main cause of severe destabilising the entire oceanic food-chain.
4) The IPCC reports which are based on a range of model outputs suffer from a high range of uncertainty because the models are not able to implement appropriately a few large scale natural oscillations such as North Atlantic Oscillation, El Nino, Southern ocean oscillation, Arctic Oscillation, Pacific decadal oscillation, deep ocean circulations, Sun's surface temperature, etc. The problem with correlation between historical observations of the global averaged surface temperature anomalies with greenhouse gases forces is that it is not compared with all other natural sources of temperature variability.
5) If we look at micro-physics of carbon dioxide, theoretically a certain amount of heat can be trapped in it as increased molecular kinetic energy by increasing vibrational and rotational motions of CO2, but nothing prevents it from escaping into space. During a specific relaxation time, the energetic carbon dioxide comes back to its rest statement.
6) As some alarmists claim there exists a scientific consensus among the scientists. Nevertheless, even if this claim is true, asking the scientists to vote on global warming because of human made greenhouse gases sources does not make sense because the scientific issues are not based on the consensus; indeed, appeal to majority/authority fallacy is not a scientific approach.
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Link to the discussions of Global Warming (Part 1):
Link to the discussions of Global Warming (Part 2):
Link to the discussions of Global Warming (Part 4):
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A great threat for the planet .......
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When plotting the global stream function at 500hpa. I notice that for the southern hemisphere, the values are positive and for the north they are negative. What does this mean physically ?
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Please consult the definition of a stream function provided by American Meteorological Society
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Does the Harp weapon cause earthquakes?
What are harp applications?
What is Harp in the climatology?
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Chris Gueymard thanks dr
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If I have 10 years of mean temperature (2001-2010) and evaluate its performance against station observations and finally calculate the climatological bias correction for each month. Can I use these values for the whole period of the CRU dataset (1901-2015) to reconstruct the mean temperature time series using the CRU?
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Dear Gabriel,
Thanks alot for your message. It depends on stations I have and number of missing observations. Of course 30 years are good to construct a linear regression model between the CRU and observations, but according to status of each station, it depends on long term availability and number of missing observations, so I need to period one for evaluation and the other one for validation
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My field of expertize is in CFD and not in climatology. But I would start a discussion about the relevance of the numerical methods adopted to solve physical models describing the climate change.
I am interested in details in physical as well as mathematical models and the subsequent numerical solution.
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I will compile a list of the most promising models and methods shortly.
The current Climate Models are relatively complex but there is still a significant potential for their improvement with respect to numerics and parameterization of unresolved scales. The idea of using a finite volume approach is very good considering their conservation properties.
The most uncertain element in the climate models is parameterization of clouds and aerosols.
As far as the optimum transportation theory in climate research is concerned this is a new idea and I’m still working with the basic formulation.
Concerning the question about the transport of CO2 this is a classical problem of atmospheric chemistry and I think that any conservative and non-oscillatory advection diffusion scheme can be used for this purpose.
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I am assessing the quality of several probabilistic streamflow forecast systems. I am using the CRPS skill score, which compares the forecast system with climatology using the CRPS. As I have only a finite sample, the value I get is a random variable. In order to assess if the CRPS skill score is really greater than zero, I would like to know the sampling properties of this metric.
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Thanks Sam Hatfield!
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i have some meteorology data which i want to import them in ANN as excel file. but i have problem with them. they variate by spatial.
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what's your problem?
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Hello! I am an archaeologist interested in paleoenvironmental studies at very small spatial scales in the Argentinean Puna. Basically, I'm trying to establish if there is a correlation between South American Monsoon System intensity and current precipitations in my specific study area. There is some debate as to whether or not this is the case...
I thought that I could run a regression between a "SASM index" over the last decades and precipitation records in regional weather stations, similarly to what has been done to test the influence of ENSO in the same area. Do you have any suggestions as to where to begin? Bear in mind that I am not a climatologist, so sophisticated climatological models are out of the question.
Thank you in advance for your help!
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Ari Jokim?ki
Guillermo Auad
Thank you both!
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Since 1988 to present began The Scientific Consensus on Climate Change. Over this period of time we have not advanced neither in climatology nor in other related sciences. Hydrology,meteorology,seismology all related sciences in the same state as 40 yeas ago.
Science is the ability to build models. Models are patterns. And the patterns somethimes are wrong. Science is the ability to be wrong.
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The yardstick with which we measure the progress of atmospheric sciences in our time is the predictability across the scales from minutes to weeks and in extreme cases to seasons. Even if this measure seems to be unreasonable, there is agreement on using it to judge our models. In the field of numerical weather forecasting, the progress is spectacular, as the recent review by the group of leading scientists shows:
At the same time, there are voices pointing out the severe limitations of our "across the scale" forecasts, as the atmospheric system is chaotic (this fact was discovered by Lorentz in 1960s)
The situation in climate modeling is even more complicated, but it is agreed that our models are sufficiently accurate to make a climate prediction based on known boundary and initial conditions. Most importantly, in climate modeling, knowledge of the boundary conditions is crucial, and actual progress depends on how accurately we can evaluate them; this subject requires the extensive interaction between all fields of geosciences.
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In recent years, ecological innovations have also been created on the basis of other non-ecology science. Ecological innovations are mainly created, among others, in the field of renewable energy sources, improvement of waste segregation techniques, recycling, treatment of polluted water, reclamation of a devastated natural environment, energy-efficient construction, electromobility etc. However, more and more eco-innovations, new technological solutions, technical improvements that fit in sustainable pro-ecological economic development also arises in many other fields of science.
In view of the above, are examples of pro-ecological undertakings, technological improvements compatible with sustainable ecological development or ecological innovations that are also created in one of the following or other related fields of science, for example: Molecular Biology, Biotechnology, Anthropology Theory, Medicine, Electrical Engineering , Artificial Intelligence, Genetics, Business Administration, Risk Management, Big Data, Business Intelligence, Automation & Robotics, Climatology, Agriculture, Biophysics, Biochemistry, Medical Intelligence, Artificial Neural Networks, Ecosystem Analysis, Power Engineering, Construction, Food production, Forest ecology, Biology, Geoscience, Government Programs, Behavioral Sciences, Biodiversity Assessment, Green Architecture, Greenhouse, Waste, Household 顺心彩票, Information Society, Innovation Management, International Entrepreneurship, Internet Technologies, Knowledge Creation, Bioelectric Energy gy Sources, Business Model Innovation, Cataclysmic Variables, Chromatography, Clean Energy, Cleantech, Climate Prediction, Collaborative Innovation, Commercialization, Computational Intelligence, Computer Science and Engineering, Conservation Biology, Consumer Behavior, Corporate Governance, Creative Economy, Crisis Communication, Cyber ??Security, Data Analysis, Database Administration, Development Cooperation, Digital Ecosystems, Discovery, Earth Sciences, Earthquake Forecasting and Geocataclysm, Econometric Analysis, Economic Integration, Economics of Innovation, Ecosystem Engineering, Electricity, Electronic Systems, Energetic Materials, Energy Technologies, Environment, Environmental Biodegradation, Flora, Food Consumption Life Sciences, Logistics Management, Materials for Sustainable Energy, Astronomy & Astrophysics, Nanomaterials, New Media Technology, Recycling, Physics of Global Warming, Plant Protection, Predictive Analytics, Production Planning, Project Development, Public Eco nomics, Public Policy, Public Policy, Sociobiology, Space Science, Sustainable Agriculture, Sustainable Development Strategies, Technology Forecasting, Transport Economics, Water Resources, Weather Forecasting, Wildlife Conservation, World Economy, ...?
In view of the above, the current question is: Will ecological innovations be created in the 21st century on the basis of other sciences besides ecology?
Please, answer, comments. I invite you to the discussion.
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Ecological inventions are need of the world to prevent destruction of the earth. Therefore we can hope more and more eco-friendly inventions will appear in 21st century.
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Am trying to simulate wave conditions over the Gulf of Guinea using SWAN Model which I intend to force with COSMO Model data
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Aswin Dinakar Ok, thank you
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I have raw radar data for a year in the Indonesian maritime continent, with a 10 minute scan time. From this data, I want to make vertical profile of reflectivity (VPR) for detect bright band and apply its correction in quatitative precipitation estimate (QPE). Because I just learned to process raw data, I have something to ask about VPR. 1. Is it necessary to separate the rain type every 10 minutes to make VPR climatology? Stratiform and convective. Or is it directly calculated without seeing the type of rain? 2. Does each pixel have its own VPR? 3. What the best elevation is used for QPE radar?
I have tried using 100 files, but the results are far from theory as attached file
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Dear Peter :
Bright band is caused by ice melting in stratiform conditions, and is much less marked (or not detectable in convective conditions). You may view to separate first stratiform and convective (presence or not of bright-band and/or horizontal gradient), before to determine VPRs. Concerning the VPR estimation, the radar beam characteristics (diameter) are important when the range increases. It is not required to determine a VPR fo each pixel. The best elevation is the lowest non affected by ground clutters. The problem with VPR is less obvious to solve when range increases because data at the lowest elevation are polluted, and because the beam vertical resolution deteriorates. It prevents to well determine the local VPR, and imposes to extrapolate a VPR derived close to the radar or to use a VPR identification model/method.
Cordialement
Hervé Andrieu
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For climatological studies, it is very important to address the issue of threshold selection. For extreme rainfall/temperature time series, apart from block Maxima series, POT (peak over threshold) series also provides vital informed. The question arises about selection of threshold value for analysis. Some literatures state that the threshold value shall be computed corresponding to a base period which is consistent with recent World Meteorological Organisation (WMO) operational climatology base period, i.e., at present 1961-1990 or 1971-2000 is the current base period adopted by WMO. For this base period, threshold values corresponding to 95th or 99th percentile values of a variable may be derived by fitting suitable probability distribution function.
The question is whether this method is robust for such estimation. If my data length of concern is 1901-2013, then the threshold values determined for the 'base period say 1961-1990' would be applicable/suitable to analyse the change during other periods, say for the years before 1961 and after 1990?
Please suggest or advice in this regard. Thanks in advance.
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Priyank, I would say that while you should consider using conventions such as a 1971-2000 base period, there is no need to be a slave to them. Since you have data spanning a much longer time, (1901-2013), it probably makes sense to treat that full 113 years as your base period to start with - unless there is a clear trend over that time. If there is such a clear trend, then the question of what section of the period to take as a "base" becomes harder - but still mostly arbitrary. You could de-trend your data (in fact you should probably do this anyway), and then look at extremes (2 or 3 standard deviations, or the equivalent on a gamma distribution of precipitation) relative to the de-trended series. Once your data is de-trended, any 30-year base period should be equivalent to any other in any case.
However, running statistical time-series analyses tends to be fairly fast, so it should not be too hard to experiment with different base-periods, and different base-period lengths. You should be guided by the data themselves, and do whatever makes most sense to extract the signals in them from the noise. E.g., some phenomena are "oscillating", and can be fruitfully analyzed in frequency space (via Fourier transform) - think of energy spectra - while other phenomena are more "episodic" (e.g., extreme rain events), and are better analyzed with "composites" rather than frequency spectra. Choose your statistical too to suit the characteristics of your data.
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When looking at the effects of climate change on modeled streamflow, is the amplitude of the seasonal cycle (maximum month - minimum month, from an annual climatology) a useful metric? I can imagine this would matter for ecosystems who rely on streamflow velocities falling within a certain range, or for reservoir operations in general, but I'm struggling to find articles discussing it. Does anyone know of a good source addressing the importance of seasonal amplitude? Or is this not an important consideration?
Thank you!
Meg
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Dear Megan,
the importance of the seasonal amplitude of streamflow is enormous from an ecological standpoint in many systems, e.g. in non-perennial rivers and streams (NPRS) (see e.g. Skoulikidis et al. 2017 and references therein). This was even one of the main points determining the evolution of the concepts of ecologically sustainable flows from a minimum vital flow to environmental or ecological flows since the 1990s.
Related to this is the ecological relevance of the spatial and SEASONAL extent of Water-Level Fluctuations (WLF) in lakes and reservoirs (e.g. Leira & Cantonati 2008, attached) and of level drawdowns in rivers.
Another example is the large relevance of seasonal variability of discharge in special habitats such as springs that are often biodiversity hotspots in spite of typically small size (see e.g. Cantonati et al. 2012 attached).
Best wishes.
Marco
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In your opinion and based on your experiences, what is the best programming language for environmental studies and modeling (e.g. climatology, hydrology, etc.)?
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I'd say Python and R. Python when you wanna deal with hydrological models (e.g. CMF a very flexible, open source model). R is very useful with long term time series and you can work in several dimensions (time + 3D spatial data).
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I have plotted climatology using tropflux data during 2014-16. It shows Arabian sea during DJF high.
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Have to verified LHF with surface wind and SST ?
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I have yearly mean SST (3D: lon X lat X time) data for 100 years. Using this data I want to calculate yearly anomaly SST data. What is the procedure to do this in ferret?
Answer is expected: how to do it using "repeat" loop in ferret over time-steps and subtracting climatological mean from every time-steps. (Any other solution is also ok)
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Hello Subrota Halder,
To save yourself from stress.
Install "cdo" using
sudo apt-get install cdo =============> (on linux)
To convert monthly data to yearly data (assuming the name of the original monthly data is "SST_monthly.nc ")
cdo yearmean SST_monthly.nc SST_yearmean.nc (ignore this is your data is yearly already)
To calculate the long-term mean use:
cdo timmean SST_yearmean.nc SST_clim.nc
To calculate the yearly anomaly use:
cdo sub SST_yearmean.nc SST_clim.nc SST_anomaly
If you insist on using Ferret, use this example
Cheers.
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Hi everyone
I have an article that will be published soon. In this paper, precipitation is predicted by MPI-ESM-MR model in Tabriz (The largest city in northwestern Iran). The results showed that, according to the both RCP4.5 and RCP8.5 scenarios, the winter precipitation will be increase over the three future periods (2021-2040, 2041-2060 and 2061-2080). Even in the RCP4.5 scenario, the trend will be ascending. While Tabriz's winter precipitation trend is descending in the base period until 2015! It seems to be a contradiction. This means that over the next three years, the winter precipitation will suddenly rise! Is this possible?? What's your opinion?
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Models of any kind are either research/simulation models or predictive/forecast models or hybrid. GCM's have only a small amount of predictive value, depending in part on what variables they are used to forecast and for how long into the future. On the other hand, I think we need to learn to use the valuable information they give us about plausibility of future scenarios. Single predictive runs, and especially ensemble predictive runs, can tell us that a period of increased precipitation in one region may be very plausible, or very implausible, or somewhere in between. Such "predictions" are more reliable than a literal reading of one forecast for a single variable, a single year, and a single site, which should almost never be taken literally.
As a society, we need to have contingency plans for all plausible futures. This may sound undesirable difficult, but it is reality. We plan baby clothing before we know the gender of a child; we plan how to spend a salary even before we are offered the job; we invest in international moves before we have a visa. So the wise citizens of Tabriz are well advised to keep both their umbrellas and their cricket sets. I'd be nervous to give up either one in our changeable climate system.
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Precipitation data of 31 years has been entered in MAKESENS excel template to obtain the result.
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Dear Suman
You can use XLSTAT. That is an extension installed in excel and very easy and useful.
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The Northward movement of the ITCZ (Inter-Tropical Convergence Zone) which reduced the number of dust rain storms with a simultaneous northward movement, and caused a rise in the level of the Nile and with it an increase in the supply of sand to the Mediterranean.
How an i measure the impacts of ITCZ on Egypt?
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Hello Ismael,
Check if you can adopt the method used in this paper over your study area.
"A study of the impact of the Intertropical Convergence Zone on aerosols during INDOEX"
Cheers.
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Hi!
I want to use CESM data to drive WRF simulations in climate change scenarios. I want to first bias correct the CESM data by using NCEP FNL data (I want to correct the mean and variance values for each simulation cell).
However, both products are on different grids and they have different vertical coordinates.
I am aware of the existence of CESM bias-corrected data, but I wish to give this a try before using it.
I was thinking on using metgrid to interpolate these datasets to my WRF grid and to use its output to do the corrections, but this would only work if they had the same vertical levels, I think.
I have the idea of comparing both datasets as closely as possible to their original versions, as doing so after lots of processing could be a source of error.
What would a correct approach for this be?
Is there any bibliography that you can recommend?
Thanks!
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Hi Argel,
You can use the method of Cannon published in January 2018. It is a latest approach and you can use it for N-dimensional data bu using
N-dimensional probability density function transformation. One advantage of this approach is it will preserve the dependence structure between climate variables which is very important for impact assessment studies. For further detail please see the link below:
Best of luck.
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Hi everyone
I'm currently working on predicting the climate change impacts with LARS-WG statistical downscaling model. LARS-WG supports existing models of CMIP5. Given that, the GCM models are very diverse. I should compare the historical period of the model with the observation period of my area. To see which model is suitable for my area. I saw this in some articles. My problem is that I do not know where I can get historical data about GCMs. The data I need is the maximum and minimum temperature, precipitation, sunshine and solar radiation.
Thank you so much for advising me.
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do not try to choose a GCM but use multiple GCMs. There is no good GCM but the use of ensemble average of the GCMs is recommended in several research studies
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We are interested in understanding long-term variations and trends of dust storms. What scientific methods and satellite products can be used to reconstruct long-term dust climatology database? Thanks.
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Is there any implementation available for the paper "Ruggieri, E. (2013). A Bayesian approach to detecting change points in climatic records. International Journal of Climatology, 33(2), 520–528. http://doi.org/10.1002/joc.3447"?
Any language is fine.
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I am trying to downscale CMIP5 climate models for RCP4.5 and 8.5.If there is any code in matlab to downscale the .nc files.
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I know I am very late.. but you can check this out:
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The potential energy of the ice caps as an additional energy source for accelerate the deglaciation and global warming
In modern climatology general scheme of the global hydrological cycle and energy budget of the Earth have several disadvantages that exist for over a hundred years. But, sooner or later, they must be corrected. Now is the time to do it. What the mistakes we talking about? This is because the above schemes did not include the huge potential energy accumulated in the glaciers. The energy accumulation accompanied by accumulation of ice. That lasted thousands of years. Since the beginning of deglaciation the ice turns to water which escapes from a certain height. As a result, the potential energy of the ice turns into kinetic energy of water. The total potential energy on the start the melting of glaciers is determined by average height of the glacier, the total weight of ice and gravity. That is general equation of Newton physics - mgh.
How much energy had been realized since the melting of glaciers? It is easy to calculate. Over twenty thousand years have passed since the beginning of quick glacial melting, simultaneously the sea levels rose to 120 meters. Thus, this value is multiplied by the area of the water surface of the Earth and on the average glaciers height (about 2000 meters). Taking into account the gravity we find the total energy released from glaciers over last twenty thousand years. Perhaps, it will be many.
Today the annual volume of energy released from glaciers can be calculated only for Greenland. From research we know that every year about 200 cubic kilometers of water from the glacier goes into the Ocean. This amount is about 70 percent water coming from all modern glaciers, together with Antarctica ice cap.
Multiplying all values we find quantitative amount of the annual release of energy. It is 4 000 000 000 000 000 000 Joules. or 1,111,111,111 MWh. If we take for comparison most powerful hydroelectric in the world Three Gorges Dam (China) - 22, 5 MW then this energy it will produce over 49,382,716 hours, or 2,057,613 days. It ultimately equals 5637 years. All together of the world hydroelectric energy produces such energy somewhere for a thousand years.
Despite the fact that in compare with total energy budget of the Earth these values, at first glance, are miserable. But here we must remember that this energy is working on a very limited area of melting glacier. Today it is about 50 000 km2 for Greenland. And all the energy goes to further the melting glacier. That is, it operates in the most vulnerable areas.
Research the melting of Greenland glaciers shows that within the general area of melting the water initially collected in the supraglacial lakes and then quickly falls down through the channels in the body of ice. These inlet channels called moulin. Their area against the background of the ice surface, probably are less than 1%. In reality, the space concentration of energy is many more.
That's why this energy requires consideration when studying other territories and the causes of global warming. Confronting of it is not possible, but the accounting of this energy permits make more accurate predictions of events for the future.
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Dr. Henrik and Mr. Wasyl,
Very good discussions on glacier and energy. We are learning. Thanks and regards
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Hi,
Could you please provide some information on Indian radars used for meteorological data collection and analysis, weather forecasting etc.? What are the characteristics of these radars?
Thank you!?
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radar can be classified in different categories based on different parameters.
based on pol ( single pol dual pol )
based on signal type ( pulsed or continuous )
you can get introduction of above classification on
this radar generally use u different parameters for study of hydrometeors also mentioned in attached presentation file.
what kind of details you want ?
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Dear All,
I'm conducting research on climate change impact on thunderstorm in Thailand. Most of the research papers I've read was conducted in Europe and United states region and almost none in Asian region. Most of the papers referred to the future projected output from RCMs such as COSMO-CLM, NARCCAP, etc. However, what I've found on the data distribution website was only the precipitation, temperature and other variables. None of the stability or thunderstorm indices was found.
I think I misunderstand something or some concept of acquiring the data. Is it possible to get those indices in Asian region? or it is available only in Europe and USA?
Please kindly advice me.
Thank you in advance.
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Dear?Jitendra Kumar Meher ?and?Philbert Modest Luhunga,
Thank you for your good suggestion.
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Hi all
This is Suman from Indian Institute of Technology Kharagpur, India. Recently I was doing eof analysis of monthly rainfall data during 1951-1980 (12*30years=360 months). I have performed the analysis using command data operator (cdo) which is a popular tool in this field. I am not able to interpret the spatial distribution of the different eof mode. How to understand that what is physically means? For example, my 1st mode is explaining about 72% variances and shows similar kind of spatial distribution as the climatological mean during that period. I am in two minds that whether the result is correct or not? Eventhough it is correct what physical process it represents?
Any sort of guidance will of immense help to me.
Suman
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Hello
If you are computing EOF on monthly data, it is not a big surprise that the first mode explains a lot of variance and is associated with a monopolar variation across your network. It simply reflects the fact that all Indian landmass (or almost except extreme SE and perhaps Kashmir) shares the same annual cycle and that the temporal variations associated with this clock overwhelms all other type of variations. If you want to concentrate on interannual variations, you need to remove the annual cycle. You can either remove the climatological monthly means and perform the EOF on the anomalies or sums the seasonal rainfall for JJAS for example to consider the main rainy season across India and then compute EOF on the seasonal amounts, which, by definition, filter our the mean annual cycle.
Vincent
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I am working with some hydrological model for which i need gridded precipitation data. I have very few observation stations and hence want to combine precipitation climatology from satellite or some reanalysis product with observed value in order to catch more spatial variability of precipitation. Any suggestions.
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A method for assimilating station data into the gridded dataset which has been proven to improve the quality of gridded data set was developed and tested (Chaney et al., 2014; Sheffield et al, 2006; Funk et al., 2015) and has been used several times to merge available station data into the full gridded dataset.
You can also use geoclime software
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I want to select best CMIP5 models for prediction of extreme precipitation events under changing climate in different climatic zones. What methodology and tests should I do to select them for observed data.
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Dear Ahmed there is no best single model especially on predicting precipitation. Different models behave differently. I would advice you to use several models to see their variability.?
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I am trying to do research on climate extreme variability in Nepal. For that, I choose the observed?daily?time series data for the 1970-2015 period, but there are many missing values of temperature and rainfall. How can I fulfill those daily values for different stations??
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Considering ensemble based forecasting of let's say stream flow, which one is better; a forecast with high ensemble spread or ?low ensemble spread. Its a bit ambiguous: low ensemble spread could mean that all the ensemble members are agreeing and the forecast could be more close to reality but it could also miss the real value by big amount. On the other hand, high ensemble spread could mean that there is high uncertainty but the real value could be forecast within a the ensemble range. So what is a better forecast, high spread or low spread??
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Hi Michael,
in addition to the answers already given:
It is often said that the spread of the ensemble members around the ensemble mean should equal the root mean square error of the ensemble mean forecast. However, this is only true if the forecast system and model are all perfectly realistic.??
Another simple requirement for a good forecast system is to ensure that the total variance in the ensemble predictions (ensemble mean variance plus ensemble spread about the mean) matches the observed historical variability.
Best
Adam
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Which precipitation product is better for trend analysis of higher Himalayan region? And do reanalysis and satellite based precipitation estimation incorporate solid precipitation?
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Dear Dr Pankaj,? One of the key issues in understanding precipitation variability over the Himalaya is lack of adequate observational network besides complexity of topography. ?Without assimilating observed precipitation data ?into reanalysis data? may lead to large errors. Many papers have been published on the issue of precipitation simulation on the mountainous topography. Even if precipitation data is assimilated, the errors?still can be large?due to topographic variations.
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I am studying the effects of this snowfall episode in the northwest of the Iberian Peninsula, where it was devastating and more than 30 people died as a consequence of avalanches, among other associated events... Nevertheless, I hardly find any information about its effects (except in the northeast coast of the United States...).
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Thank you Kenneth, yes, my research is based on newspapers and other historical sources (church records), and on fieldwork as well. But I was looking for some information on other places (outside the Iberian Peninsula or the USA), where the snow could have caused damage,?just to contextualize my results ... I also think that the effects on the East Coast of the United States?hid what happened in other places, thank you very much for your kind answer!
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Any smart way to correct elevation of monthly mean era temp data for Himalayan catchment?
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Thank you Dan
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We are looking for papers that deal with the use of automated earth weather stations (AWS) for the recording of environmental parameters of cultural heritage sites or palaentological sites.
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Very nice report Krishnan, thank you.
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kindly let me know trend analysis for precipitation and temperature data is mandatory prior to calibration and validation of hydrologic model followed by simulation for climate change impact on water resources or it can be skipped as i am carrying out bias correction for my RCM Data also?
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Air temperature from LST (day and night).
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it exist the formulas from landsat using zenithal angle and insolation angle to retrieve the values of AIR temperature. it's very usefull and i will send you letter.
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I saw some models (ACCES1-0, GFDL-CM3, CCSM4) of CMIP5 for the specific humidity variable have undefined values ??at the level of 1000hPa on the continents. Why does this happen? Thank you.
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It is still almost certainly the 1000 hPa surface intersecting with the land/ocean surface as this is the time when the Australian monsoon is at its peak (boreal winter / austral summer). The region is almost entirely in the Southern Hemisphere too, which is again consistent with the above.?
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?I am working on a project to develop a statistical model to predict district-wise rainfall of India. I have 100 year data on four significant factor like- ?nino?3.4, Indian ocean dipole , zonal wind speed of tropical easterly jet, surface pressure data of Madagascar basin, district wise normal rainfall data, actual rainfall data district wise all of districts of last 100 years. Kindly suggest some statistical tool which can help to predict rainfall of 2017 .?
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Belda M, Holtanová E, Halenka T, Kalvová J (2014) Climate classification revisited: from K?ppen to Trewartha. Clim Res 59:1-13. https://doi.org/10.3354/cr01204
Forsythe, Nathan, Stephen Blenkinsop, and H. J. Fowler. "Exploring objective climate classification for the Himalayan arc and adjacent regions using gridded data sources." Earth System Dynamics 6.1 (2015): 311.
Since these papers were produced, the CRU at the UEA has revised the global precipitation dataset to remove wet bias.
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Dear All,
I would like to ask how should be the lowest vertical data interpreted if they are placed below surface level? For example, in the Alpine region with the average altitude of ~1500 m asl the vertical layers of 1000 or 975 hPa are very often significantly below the surface level (if calculation are based on geopotential height). How are those values then calculated? Is there any physical meaning of those data?
Kind regards,
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The situation described in your question is typical for all conversions between the sigma type coordinates employed in the model and the pressure based coordinates used for storing, displaying and collecting the meteorological data. In the case of variables below the terrain level the problem is particularly complicated. The results will vary significantly depending on temperature profiles (they are assumed) and the constraints imposed during extrapolation of the dynamical fields. The related problems are discussed extensively in the paper addressing vertical interpolation of meteorological fields
In my opinion there are still some opportunities to improve the existing methods of extrapolation.
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I?have past 30 years observed temperature and precipitation data available. How can the climate change impact be established using observed data ?
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Is there any English copy of this research paper - (Extreme weather events under climate change- Lightning) available?
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Dear Stephan,
Thank you for your reply. I am a Masters Research student and studying under Prof Dr. Matschullat. My interest area includes Extreme climate events and modeling. This article seemed pretty enticing with the title of climate extremes associated with lightning. I only wishes to know more about it. It would be really kind of you if you could help. However, I have already got this idea that unfortunately its not available in English. Still I would gladly welcome any sort of help if you can do.
Best Regards
Monica
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two sets of data in excel are relative humidity and air temperature. from these i need to calculate vapor pressure deficit.
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Comparison of temperature lapse rates from the northern to the southern slopes of the Himalayas
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My current research project is about determining whether there is a significant correlation between the changing climate patterns in the Philippines (e.g. fluctuations in the levels of precipitation, humidity, and surface temperature) and the frequency of dengue outbreaks in different regions of the country. Do I use the coefficient of multiple correlations? Or are there other more efficient and precise ways of determining a correlation between multiple variables?
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Dear Saejj Remorque,
I think you can perform multiple regression or nonlinear regression with different methodologies to find the correlation between multiple parameters.
Recently I have performed nonlinear regression for my recent publication. Herewith I am sending you a link which may help you to understand your problem. The parameters and objectives are different than your one but maybe methodology can hep you.
Hope this can help you
Best Regards
Piyush Patel
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I am going to compare the Climate ( Temperature and Precipitation) of the two regions. I want to present my data through different models. I have data for the last thirty years. ??
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Thank you Beckline Mukete...
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Dear Sir/Friends,
Can anyone explain me how to calculate the mean frequency of daily rainfall events and std.?deviation for the SW monsoon daily rainfall events. I have extracted the grid points for my study area so how can i compute the Mean frequency and std?dev of daily rainfall data for SW monsoon.?
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Hi Vinay,
Are you using the raw data or have you calculated the probability density function? If it is the latter the formula for SD needs a modification, you should use the equation for the weighted moments, as explained in http://www.fondpageant.com/publication/313893117_Notes_on_the_method_of_Cumulants_for_solution_of_linear_equations_involving_random_variables?
See Example 2, eq. (28).
Hope it helps,
Augusto?
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please also elaborate the use of NCEP reanalysis instead of GCM historical simulation output as predictors?
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thanks a lot Patrick Laux for detailed answer.
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Dear Sir/friends,?
Am working on spatio-temporal?variation of rainfall for West coast karnataka, India. Am looking for the identification of modeling for the influence of ENSO, SST and other parameters to the preciptation?on study area. Kindly suggest me some simple recent techniques for my?research work.?
thank you?
Regards
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Thank you Sir,?Timothy O Randhir
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I have to use the GCM/RCM data for the generation of future climate variables in the Hindukush Karakorm Himalayan Region. I am not finding a relevant source on how to run the RCM data simulations. Can someone please help in this regards.
Thank you.
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Hi, is there any published paper that mentioned westerlies (pressure belt between 30 to 60 degree North latitude) shift south during the colder period and shift north during warmer (summer period).
One more paper that shows the Intertropical Convergence zone (ITCZ) during summer and winter in the Indian subcontinent. ?
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Dear?Prabhat chandra Neupane,
See Figures 7.7 and 7.8 (Chapter 7: Scale of the Atmospheric Circulations) in the book below.
Regards,
Milivoj B. Gavrilov
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In order to detect changes and effects from climate time series data and to project key climate variables for impact studies, we are facing difficulty in calibrating SDSM using regional scale atmospheric predictors of NCEP. Difficulty is very low R^2 value against selected predictors. Any expert in SDSM can help?
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I think Quantile Mapping method is bias correction method. In SDSM site you can find newly rearranged NCEP data from 1948-2015. Then you should decide which GCM will use for your impact studies.?
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Hi?everyone
What is the procedure to obtain the constants in H/Ho=a+b(S/So)? I have monthly average daily radiation and monthly average day length.
I applied by Exel, but these points in the graph are scattering. I can not get a linear relationship.?
Thanks for everybody
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To calculate?simply a multivariate regression in Excel:
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Dear Sir and Friends,?
?Am new to the reanalysis data, but i understood bit about it. Me working on spatial distribution of rainfall at 0.25deg (25KM) resolution data. So am thinking to map the CAPE over the study region?for monthly or Annual ?Average. Anyone help me to calculate the CAPE for my region in easiest way to understand the spatial variation of CAPE from ECMWF or any other high spatial resolution data. I want to represent the CAPE in 2D plot (GIS Map).?
Thank you,?
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could you please give me more exeplanation about the climate levels such as (macro climate, micro climate, local climate, meso climate and indoor climat with chart and pics?
Thanks in advance
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Maybe it will be helpful
best regards
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Why is statistical level of significance usually selected as 0.05 in trend test of climatological data?
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The choice is arbitrary. The value 0.05 comes from Sir Fisher, who wasn't allowed to print tables of critical values for test statistics for several different significances. Because of copyright restrictions he could only print an excerpt, and he decided to take 0.05 as something that should work in most cases IN HIS FIELD OF RESEARCH. He was thinking about designed experiments with relatively few replicates. It was not ment to be blindly taken over to other disciplines and other kinds of data (observational studies, large clinical trials, screenings etc.). Actually, Fisher himself was interpreting his own results not w.r.t. a fixed level of significance. Depending on the context(sample size, experimental difficulties, aim of the experiment) he sometimes considered much larger p-values as "significant" and somtimes much lower p-values as "non.significant".
Testing always at the same level of significance is actuall really irrational.
@Michael:significance is not related to the SD. At least for tests of the expected value, the test statistic, t, is the ratio of the estimate to the standard error (SE) of the estimate. For a non-zero estimate you can get arbitrarily extreme t-values (and hence p-values arbitrarily close to zero) just by increasing the sample size, becose the SE (which is the denominator) decreases with sample size. The SD is independent of the sample size. But I would not even talk about the SE. Instead, I think it is more instructive and less misleading to understand a significance test as a likelihood-ratio test. You calculate the likelihood of the observed data under a full model and under a restricted model (the restriction is the "null hypothesis"). Given some assumptions about the conditional distribution of the response, one can derive the distribution of the ratio of likelihoods under the restricted model, and this can be used to formulate an expectation about a "more extreme liklihood ratio" as the one obtained from the data (i.e. the p-value). Fisher found that the general asmyptotic distribution of (log) liklihood ratios is a chi2-distribution, and for a normal conditional distribution of the response this is an F-distribution (if the models differ by a single degree of freedom, it can be shown that F = t2, with t having a t-distribution). There is nothing related to SD (or SE). The SE is related to the curvature of the likelihood function at its maximum: the sharper the likelihood function "peaks", the smaller is the SE.
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I am looking for raster datasets covering the whole geographical Europe, with wind speed and direction. Any hint would be appreciated.
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Bonjour Renaud
10m winds from ERAINTERIM reanalysis
0.75x0.75 degree - gridded over the globe and Europe. Daily and sub-daily data is available. The daily data extraction is tricky though (due to start date). On the climate data side it's surely one of the best product available (it is used to prescribe boundary conditions for mesoscale models etc). ?Model mixed with observation data using assimilation method
Best wishes
Cyril
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I have calculated Standardized Precipitation Index values for 25 years of data. I wish to study the frequency of excess and deficit years during 25 years. Apart from the standard classification suggested by Mc Kee, some researchers use values +_0.10 or sometimes 0.5 for assessment of excess and deficit years. Could there be any explanation to this?
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Yes, have more insight in info avilable on the following web link. Best..
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Mangrove ecosystems could be affected by SST, sea level, local winds, and rainfall.
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Mangroves are local entities and therefore local rainfall? in the adjacent catchment?is probably the most important factor. However it will also be influenced by storm waves and surges by direct impacts ( erosion of sediments and physical damage to the mangrove itself.). The mangroves will be supplied with nutrients from land runoff and this is mediated by the mixing with adjacent ocean and shelf.
ENSO is a large scale phenomena which has an global influence on the atmosphere and therefore on regional and local climate.? In tropical regions it has a major influence on tropical cyclones, mesoscale phenonema eg ?thunderstorms on coast.
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I am interested to know the standard sources (reports / websites / research articles) which can be refereed to know the precise?years in which El Nino, La Nina, Positive IOD and Negative IOD impacted Indian Monsoon between 1970 to 2016.
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The BoM maintains a good record of IOD-positive/negative years (http://www.bom.gov.au/climate/iod/) which is good for an overview; though I prefer the more detailed analysis from JAMSTEC (http://www.jamstec.go.jp/frcgc/research/d1/iod/HTML/Dipole%20Mode%20Index.html).
Again for ENSO, the BoM has a good overview (http://www.bom.gov.au/climate/enso/enlist/index.shtml, with links for La Nina therein), as does NOAA (https://www.esrl.noaa.gov/psd/enso/mei/); if you want something more detailed in terms of numbers, I would recommend the CPC (http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml).
Hope these help.?
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Hi help, I would like to predict the impact of climate variability on crop yields. I have climate variables from CORDEX Regional climate models. Is it acceptable to use the ensemble average to predict inter annual variation of climate variables. I received comment that individual CORDEX RCMs forced by GCMs cannot be used to study interannual variation but are limited for long term analysis (mean climate-30years). Can I use the ensemble average may be it will capture the inter-annual variability of rainfall and temperatures?. Is this acceptable?
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Do you know any paleoclimate reconstructions for Tanzania for the last 100 to 20.000 years? How stable has the climate been during that period? This kind of information might help you to interpret whether the model runs for the future are plausible or not.
In the linked publication, it looks like internal variability dominates but models may not be doing a good job over the region.
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Hello,
I want the North Atlantic Oscillation (NAO) index data for all the CMIP5 or AR5 GCMs in monthly or seasonal format during 1901-2015. Please suggest me a data archive to get this data.
Thanks in advance.
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Hi Jitendra... You can download SLP fields from ESGF database (https://esgf-index1.ceda.ac.uk/search/cmip5-ceda/) and subtract values from 37.7°N, 25.7°W (Azores) and 65.1°N, 22.7°W (Iceland).
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I need daily gridded maximum and minimum temperature data on daily scale to run a hydrological model (SWAT model). Where can I can get data over global domain or over India? Indian Meteorological Department (IMD) does not provide these data freely. What is the other reliable and free sources where I can get those data?
Thanks.
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need daily gridded wind speed and solar radiation data on daily scale to run a hydrological model (SWAT model). Where can I can get data over global domain or over India? What are the ?reliable and free sources where I can get those data?
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I've been using the gridded solar irradiation data from MSG for Africa as provided by the CMSAF. I'm looking for an equivalent for South America and Oceania.
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The NASA-SSE database covers the whole world on a dialy basis but does not offer recent data.
Except re-analyses, which I do not recommend, I know only commercial products covering these areas: SolarGIS and 3Tier by Vaisala.
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Hi,
I am well aware of the different sources for seasonal climate information. However, I am wondering if there is any database (server) where I can easily download such data, just by selecting the region, forecast horizon, data source, etc.
Any hints appreciated ...
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Thank you, Elke. These I know already;-).
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Dear all,
I am looking for daily minimum temperature data (50 years back in time) for Russia and Japan.
Any suggestions highly appreciated.
Cheers
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Dear George,
very good idea. I will check if I can access.
Thanking all of you!!!!
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I have two landslide rasters/gridded data (1990 and 2010 with landslide number but does not have information of when event has occurred). I want to relate these landslide rasters with gridded? precipitation to understand the influence of rainfall on landslide. How can i do this?
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See here...just published 7 days ago:
Statistical approaches for the definition of landslide rainfall thresholds and their uncertainty using rain gauge and satellite data
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While there are plenty of mean sea surface temperature reconstructions for geological time periods, I could not find data on present-day temperatures. I have searched the web for global mean annual sea surface temperature (for the last years, as well as for a 20th century average), but the only values I could find concerned the temperature difference from the average. But what is the average? I'd be glad for any help!
Thanks, Thomas
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U may refer ICLARM