I'm trying to reconstruct the time series of a weather station over the whole 1991-2020 period by using an external model as predictor.
I've successfully used ERA5-Land as predictor for temperature as the correlation with station data is quite high, however I'm struggling to do the same for precipitation.
What is the best alternative to reanalysis in this case? I was thinking about datasets of daily precipitation measured from satellite but the data seems to be quite coarse.
In my case I have the following requirements
- Temporal coverage from 1991-01-01 onwards (at least until 2020-12-31, but extension to present is quite important)
- Daily accumulated precipitation
- Grid spacing lower than 0.25 degrees
- Coverage of the Mediterranean area
You have any suggestions on which dataset I could use?
Hi Guido,
I haven't used it but there is an Ensemble Meteorological dataset (EM-Earth) from 1950-2019, which was published about a year ago (https://journals.ametsoc.org/view/journals/bams/103/4/BAMS-D-21-0106.1.xml), apart from the popular ones like CHIRPS and MSWEP (https://www.gloh2o.org/mswep/).
Try NASA_GPM_IMERG daily data at 10 km resolution.
Link: https://search.earthdata.nasa.gov/search
Hello Guido,
try The Satellite Precipitation - CMORPH Climate Data Record (CDR) consists of satellite precipitation estimates that have been bias corrected and reprocessed using the the Climate Prediction Center (CPC) Morphing Technique (MORPH) to form a global, high resolution precipitation analysis. Data is reprocessed on a global grid with 8km-by-8km spatial resolution. Temporal resolution is 30 minutes over a 20 year period of record (January 1998–present). The output precipitation fields are produced on three different time-space resolutions to accommodate a variety of user requirements.
https://www.ncei.noaa.gov/data/cmorph-high-resolution-global-precipitation-estimates/access/
- Badges
- Science topic
- Similar topics
- Thesis Research