Precipitation

As precipitation from weather simulation data can show substantial deviations, the implementation of measurements is of major importance to reach the highest accuracy level. Real time observations from radar systems are used for precipitation nowcasting. To estimate historical precipitation there are data composites from satellites and measurement stations.

meteoblue offers the following datasets:

  • Real time radar & nowcast - available for many countries
  • CMORPH (NOAA) - Hourly precipitation history, worldwide
  • CHIRPS2 (CHG) - Daily precipitation history, worldwide
  • ARC2 (NOAA) - Daily precipitation history for the African continent

Real time radar & nowcast

Precipitation nowcast data shows weather data for the present and near future (1-6 hours). These are observed with radar and show the best information available for the current conditions.

Simulated weather forecasts (data packages) obtained by the meteoblue API will always include satellite, radar and measurement data automatically, if those data are available for the selected location. The accuracy of a precipitation nowcast is highly dependent on the availability, resolution and delay of the radar data, which differs for each country. The following table gives an overview of the availability of real time radar data: 

Classification / Country Public Data Delay of last available observations Nowcast Range / Forecast duration Nowcast Interval / Temporal resolution Spatial resolution
Spain yes 15 minutes - 15 minutes 1 km
France yes 60 - 85 minutes - 15 minutes 1 km
Switzerland yes 10 minutes 60 minutes 15 minutes 1 km
Germany yes 5 minutes 90 minutes 10 minutes 1 km
United Kingdom yes 30 minutes - 15 minutes  
USA yes 5 minutes 60 - 90 minutes 10 minutes 1 km

CMORPH - Precipitation, hourly, global

Field Metadata
Title NOAA CPC morphing technique
Short Title CMORPH
Version 1.0, 2002
Theme satellite, precipitation, estimate
Period of Time 1998 to present
Frequency daily
Update timelag < 24 hours
Time-resolution hourly
Spatial Type Plate carrée grid (rectangular projection)
Spatial extend 180°W to 180°E / 60°S to 60°N
Spatial resolution 8 km at the equator
Coordinate system WGS-84
Earth model WGS-84
Spatial reference system  
Publisher NCEP/NOAA
Date First Published 2002
Date Obsolete  
Description

CMORPH produces global precipitation analysis at very high spatial and temporal resolution. This technique uses precipitation estimates that have been derived from low orbiter satellite microwave observations exclusively, and whose features are transported via spatial propagation information that is obtained entirely from geostationary satellite infrared data. At present we incorporate precipitation estimates derived from the passive microwaves aboard the DMSP 13, 14 & 15 (SSM/I), the NOAA-15, 16, 17 & 18 (AMSU-B), and AMSR-E and TMI aboard NASA's Aqua and TRMM spacecraft, respectively. These estimates are generated by algorithms of Ferraro (1997) for SSM/I, Ferraro et al. (2000) for AMSU-B and Kummerow et al. (2001) for TMI. Note that this technique is not a precipitation estimation algorithm but a means by which estimates from existing microwave rainfall algorithms can be combined. Therefore, this method is extremely flexible such that any precipitation estimates from any microwave satellite source can be incorporated.

With regard to spatial resolution, although the precipitation estimates are available on a grid with a spacing of 8 km (at the equator), the resolution of the individual satellite-derived estimates is coarser than that - more on the order of 12 x 15 km or so. The finer "resolution" is obtained via interpolation.

Detailed specification

Joyce, R. J., J. E. Janowiak, P. A. Arkin, and P. Xie, 2004: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydromet., 5, 487-503.

http://journals.ametsoc.org/doi/abs/10.1175/1525-7541%282004%29005%3C0487%3ACAMTPG%3E2.0.CO%3B2

Homepage URL http://www.cpc.ncep.noaa.gov/products/janowiak/cmorph_description.html
Security Classification public
Access Level https://www.usa.gov/government-works
License public

 Current dataset issues

CMORPH is derived from polar orbiting satellites and therefore data gaps are quite frequent, with an estimated data coverage of 95 %. Missing data are flagged with ‘NaN’ values and can be replaced with NEMS30 or ERA5 using the “gap filling” function.

CHIRPS2 - Precipitation, daily, global

Field Metadata
Title Climate Hazards Group Infrared Precipitation with Station data
Short Title CHIRPS2
Version 2, 2015
Theme satellite, precipitation, estimate
Period of Time 1981 to present
Frequency daily
Update timelag < 24 hours
Time-resolution daily
Spatial Type Plate carrée grid (rectangular projection)
Spatial extend 180°W to 180°E / 50°S to 50°N (origin: -179.975/-49.975)
Spatial resolution 5 km (interpolated to 0.05 deg)
Coordinate system WGS-84
Earth model WGS-84
Spatial reference system  
Publisher CHG (Climate Hazards Group)
Date First Published 2015 (first version 2013)
Date Obsolete  
Description CHIRPS is a 30+ year quasi-global rainfall dataset. Spanning 50°S-50°N (and all longitudes), starting in 1981 to near-present, CHIRPS incorporates 0.05° resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring.
Detailed specification

Funk, Chris, Pete Peterson, Martin Landsfeld, Diego Pedreros, James Verdin, Shraddhanand Shukla, Gregory Husak, James Rowland, Laura Harrison, Andrew Hoell & Joel Michaelsen.
"The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes". Scientific Data 2, 150066. doi:10.1038/sdata.2015.66 2015.

https://www.nature.com/articles/sdata201566

Homepage URL http://chg.geog.ucsb.edu/data/chirps/
Security Classification public
Access Level https://www.usa.gov/government-works
License public

Current dataset issues

CHIRPS2 is derived from polar orbiting satellites and therefore data gaps are quite frequent, with an estimated data coverage of 95 %. Missing data are flagged with ‘NaN’ values and can be replaced with NEMS30 or ERA5 using the “gap filling” function.

ARC2 - Precipitation, daily, Africa

Field Metadata
Title African Rainfall Climatology, version 2
Short Title ARC2
Version 2, 2012
Theme satellite, precipitation, estimate
Period of Time 1983 to present
Frequency daily
Update timelag < 24 hours
Time-resolution daily
Spatial Type Plate carrée grid (rectangular projection)
Spatial extend 20°W to 55°E / 40°S to 40°N (origin: -20.0/-40.0)
Spatial resolution 10 km (interpolated to 0.1 deg)
Coordinate system WGS-84
Earth model WGS-84
Spatial reference system  
Publisher NOAA Climate Prediction Center
Date First Published 2012, (first version 1996)
Date Obsolete  
Description ARC2 is a revision of the first version of the ARC. Consistent with the operational Rainfall Estimation, version 2, algorithm (RFE2), ARC2 uses inputs from two sources: 1) 3-hourly geostationary infrared (IR) data centered over Africa from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) and 2) quality-controlled Global Telecommunication System (GTS) gauge observations reporting 24-h rainfall accumulations over Africa. The main difference with ARC1 resides in the recalibration of all Meteosat First Generation (MFG) IR data (1983–2005). Results show that ARC2 is a major improvement over ARC1. It is consistent with other long-term datasets, such as the Global Precipitation Climatology Project (GPCP) and Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP), with correlation coefficients of 0.86 over a 27-yr period. However, a marginal summer dry bias that occurs over West and East Africa is examined. Daily validation with independent gauge data shows RMSEs of 11.3, 13.4, and 14, respectively, for ARC2, Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis 3B42, version 6 (3B42v6), and the CPC morphing technique (CMORPH) for the West African summer season. The ARC2 RMSE is slightly higher for Ethiopia than those of CMORPH and 3B42v6. Both daily and monthly validations suggested that ARC2 underestimations may be attributed to the unavailability of daily GTS gauge reports in real time, and deficiencies in the satellite estimate associated with precipitation processes over coastal and orographic areas. However, ARC2 is expected to provide users with real-time monitoring of the daily evolution of precipitation, which is instrumental in improved decision making in famine early warning systems.
Detailed specification https://journals.ametsoc.org/doi/10.1175/JAMC-D-11-0238.1
Homepage URL https://www.ngdc.noaa.gov/docucomp/page?xml=NOAA/NWS/NCEP/CPC/iso/xml/Daily-ARC2-Africa.xml&view=getDataView&header=none
Security Classification public
Access Level https://www.usa.gov/government-works
License public

 Current dataset issues

ARC2 is derived from polar orbiting satellites and therefore data gaps are quite frequent, with an estimated data coverage of 95 %. Missing data are flagged with ‘NaN’ values and can be replaced with NEMS30 or ERA5 using the “gap filling” function.