I have grided data sets of monthly average upper air temperature, geopotential height, relative humidity, wind speeds(on 1000-200hPa levels) and i wish to investigate trends, normals high,low, thickness, atmospheric stability, long shortwaves etc.
What i can do:
(1) Use monthly average climatic variables but how can i predict my requirements from past 30 years data set?
(2) Other option is to convert my data set into annul average datasets? and then do these analysis
Problem in option 1 is dealing with monthly avergae data set on 8 different pressure levels of 30 years it will be trouble some for me as there will be a lot of maps will form for each month of 30 year, difficult to place them in research
Problem in option 2 , there are different definitions of annual average , some authors nerate it is just average of each month in a year( jan+feb...dec/12) while others write yearly average climatic variable is average of high and low value of variable appear in a year?
My grid consist on 64 locations, i am confuse which strategy will be fine for my research? Guide me with literature
Dear Muhammad,
I completely agree with Naveen Kalra. You can do multiple things with the data you have.
I would suggest to use monthly averages (the first case). You can do trend analysis or regression analysis so that you can estimate your requirements for present. The modelling is also used for simulating data based on past conditions, which can be used for your present work. Indices are used for classifying areas at regional/global scales or to assess the impacts.
For your second question, I would suggest that it varies for different parameters. Say for rainfall, when annual trend analysis is performed, they consider the rainfall of whole year. But for streamflows, annually averaged daily flows are considered. Adding monthly data and dividing by 12 is not a good idea as it may be misleading for most of the meteorological data.
I hope it helps.
All the best !
Hi Muhammad,
I think hat you have great data to play with them. First option sounds better. Think that no journals would accepr the reason "it was too much work".😉 Maybe you can try to simplify by seasons, so you would be reducing in 1/3 the aanalysis.
The Second option is also good you have 30 years, especially if you are dealng with trends. I would nevee use the avarage as the middle value between max and min. Just divide into 12. For trend analysis this could be a good option too.
Hi Muhammad,
Having a lot of data is really good but, as you pointed out, you need to find a way to downscale them and present the results. Please find below links to articles that deal with downscaling weather data and presenting the results in maps. These could guide your choice :
- Statistical downscaling of regional climate model output to achieve projections of precipitation extremes :
http://www.sciencedirect.com/science/article/pii/S221209471530058X
- Comparison of downscaling methods for mean and extreme precipitation in Senegal :
http://www.sciencedirect.com/science/article/pii/S2214581815000646
- Statistical downscaling of CMIP5 outputs for projecting future changes in rainfall in the Onkaparinga catchment :
http://www.sciencedirect.com/science/article/pii/S0048969715300802
Dear @Abhijit Mitra let i use downscaling of my data into yearly. I can simulate this yearly data into matlab for next five years. Tell me what is metastatics programme? Are you telling me about using statistics to descripbe my problem? what statistics will implement for trends, normals high,low, thickness, atmospheric stability, long shortwaves?
Software which imput these monthly data set and gives trends for atmospheric fronts, atmospheric stability, long short waves, geopotential height abnormality etc?
Will be very thankful for this favor.
computation in matlab take a bluck of time, let say i want to make skewt digram for a single file i have to write down 1000 lines of code. Some software i need also?
Dear Muhammad, unique dataset, which you can view from many angles,
monthwise mean, CV, extreme/episodic events probability
Seasonwise mean, CV, extreme/episodic events probability
Inter-annual climatic variability analysis
Inter-seasonal climatic variability analysis
Characterization of inter-seasonal climatic variability through yield on regional scales
Use of crop simation models for inter-seasonal climatic variability characterization
Use of indices like SPI, SPEI for water availability association with yield on regional scales
All these actions are well published and you and your group can work on these aspects
regards
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