Global Interpolated RAinFall Estimation (GIRAFE)

A global precipitation product became a major building block of the CM SAF vision to enable climate monitoring of the energy and water cycle in CM SAF’s CDOP 3 phase, based on the Tropical Amount of Precipitation with an Estimate of Errors (TAPEER) methods (Roca et al., 2018). Thus far, with HOAPS, CM SAF provided a precipitation product over the ice-free ocean only. The resulting GIRAFE v1 climate data record (CDR) provides 1°x1° daily accumulated precipitation as well as the associated sampling uncertainty over the globe for the period 2002-2022, and monthly means thereof. Main features in the GIRAFE processing are the combination of complementary observations in the microwave and infrared part of the electromagnetic spectrum, the homogenisation and quality control efforts on the input streams, and the above-mentioned unique sampling uncertainty for the resulting gridded daily accumulated precipitation. With its origin lying in a series of dedicated workshops “on global precipitation monitoring in a joint European effort” in the years 2014-2017, GIRAFE v1 is the first precipitation CDR generated in a pan-European context.

Top: Global map of GIRAFE v1 daily accumulated precipitation on 2021/07/13. Bottom: Global map of the related daily GIRAFE v1 sampling uncertainty. Source: CM SAF

The accumulated precipitation is the product of the conditional precipitation rate, the precipitation fraction and the duration of the day. Poleward of 55°N/S, both the precipitation fraction and the conditional precipitation rate are computed using microwave imagers and sounders Level 2 rain products from polar-orbiting satellites. For GIRAFE v1, the precipitation retrieval algorithms HOAPS (see below), PNPR-CLIM* (Bagaglini et al., 2021), and PRPS (Kidd, et al. 2021) were used. Inside 55°N/S, the algorithm trains infrared observations from geostationary satellites with the above-mentioned microwave-based precipitation rates to detect the occurrence of precipitation and derive the precipitation fraction based on these infrared observations.

The daily sampling uncertainty estimate comes in the form of a standard error, that is the standard deviation, scaled by the square root of the number of independent observations. The latter are obtained from variogram computations (Roca et al., 2010; Chambon et al., 2013).

More information on the GIRAFE v1 CDR including the links to the documentation is available on the DOI page: https://doi.org/10.5676/EUM_SAF_CM/GIRAFE/V001 .

*The PNPR-CLIM algorithm has been developed by CNR-ISAC in the C3S_312b_Lot1 Copernicus project.

Precipitation from HOAPS (ice-free ocean)

The Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite data record (HOAPS) is a completely satellite based climatology of various parameters, among them precipitation, over the global ice-free oceans. The product is derived from recalibrated and intercalibrated measurements from SSM/I and SSMIS passive microwave radiometers and utilises the CM SAF SSM/I and SSMIS FCDR. The precipitation retrieval algorithm is described in Andersson et al. (2010). The HOAPS precipitation product has global coverage, i.e., within ±180° longitude and ±80° latitude, is only defined over the ice-free ocean surface. HOAPS v4 covers the period from July 1987 until December 2014. The product is available as monthly averages and 6-hourly composites on a regular latitude/longitude grid with a spatial resolution of 0.5° x 0.5° degrees. The HOAPS product suite also includes evaporation E and the freshwater budget, i.e., E-P.

More information on the HOAPS v4 TCDR including the links to the documentation is available on the DOI page: https://doi.org/10.5676/EUM_SAF_CM/HOAPS/V002.

Instantaneous precipitation rate estimates obtained from microwave imagers over ice-free ocean using the HOAPS algorithm are used in GIRAFE v1 (see above). The HOAPS database for GIRAFE v1 features more microwave imager instruments and satellites than in the latest HOAPS TCDR version (v4) and extends the HOAPS precipitation record to 2022.

All data records can be ordered via the Web User Interface.

Publications:

• Andersson, A., Fennig, K., Klepp, C., Bakan, S., Graßl, H., and Schulz, J., 2010: The Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data - HOAPS-3, Earth Syst. Sci. Data, 2, 215-234.
• Bagaglini, L.; Sanò, P.; Casella, D.; Cattani, E.; Panegrossi, G., 2021: The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification. Remote Sens., 13, 1701.
• Chambon P., Jobard, I., Roca, R., and Viltard, N.. 2013: An investigation of the error budget of tropical rainfall accumulation derived from merged passive microwave and infrared satellite measurements. Q. J. R. Meteorol. Soc. 138: 879-893.
• Kidd, C., Matsui, T., and Ringerud, S., 2021: Precipitation Retrievals from Passive Microwave Cross-Track Sensors: The Precipitation Retrieval and Profiling Scheme. Remote Sens., 13(5), 947.
• Roca R., P Chambon, Jobard I, P-E Kirstetter, M Gosset, JC Bergès, 2010, Comparing Satellite and Surface Rainfall Products over West Africa at Meteorologically Relevant Scales during the AMMA Campaign Using Error Estimates, J. App. Met. Clim. Volume 49, Issue 4 , pp. 715-731.

• Roca, R. et al., 2020: Merging the Infrared Fleet and the Microwave Constellation for Tropical Hydrometeorology (TAPEER) and Global Climate Monitoring (GIRAFE) Applications. In: Levizzani, V., Kidd, C., Kirschbaum, D.B., Kummerow, C.D., Nakamura, K., Turk, F.J. (eds) Satellite Precipitation Measurement. Advances in Global Change Research, vol 67. Springer, Cham.

HK / April2024