The Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) products provide quasi-global (60° N–60° S) precipitation estimates, beginning March 2014, from the combined use of passive microwave (PMW) and infrared (IR) satellites comprising the GPM constellation. The IMERG products are available in the form of near-real-time data, i.e., IMERG Early and Late, and in the form of post-real-time research data, i.e., IMERG Final, after monthly rain gauge analysis is received and taken into account. In this study, IMERG version 3 Early, Late, and Final (IMERG-E,IMERG-L, and IMERG-F) half-hourly rainfall estimates are compared with gauge-based gridded rainfall data from the WegenerNet Feldbach region (WEGN) high-density climate station network in southeastern Austria. The comparison is conducted over two IMERG 0.1° × 0.1° grid cells, entirely covered by 40 and 39 WEGN stations each, using data from the extended summer season (April–October) for the first two years of the GPM mission. The entire data are divided into two rainfall intensity ranges (low and high) and two seasons (warm and hot), and we evaluate the performance of IMERG, using both statistical and graphical methods. Results show that IMERG-F rainfall estimates are in the best overall agreement with the WEGN data, followed by IMERG-L and IMERG-E estimates, particularly for the hot season. We also illustrate, through rainfall event cases, how insufficient PMW sources and errors in motion vectors can lead to wide discrepancies in the IMERG estimates. Finally, by applying the method of Villarini and Krajewski (2007), we find that IMERG-F half-hourly rainfall estimates can be regarded as a 25 min gauge accumulation, with an offset of +40 min relative to its nominal time.

To examine the claim that Global Positioning System (GPS) radio occultation (RO) data are useful as a benchmark data set for climate monitoring, the structural uncertainties of retrieved profiles that result from different processing methods are quantified. Profile‐to‐profile comparisons of CHAMP (CHAllenging Minisatellite Payload) data from January 2002 to August 2008 retrieved by six RO processing centers are presented. Differences and standard deviations of the individual centers relative to the inter‐center mean are used to quantify the structural uncertainty. Uncertainties accumulate in derived variables due to propagation through the RO retrieval chain. This is reflected in the inter‐center differences, which are small for bending angle and refractivity increasing to dry temperature, dry pressure, and dry geopotential height. The mean differences of the time series in the 8 km to 30 km layer range from −0.08% to 0.12% for bending angle, −0.03% to 0.02% for refractivity, −0.27 K to 0.15 K for dry temperature, −0.04% to 0.04% for dry pressure, and −7.6 m to 6.8 m for dry geopotential height. The corresponding standard deviations are within 0.02%, 0.01%, 0.06 K, 0.02%, and 2.0 m, respectively. The mean trend differences from 8 km to 30 km for bending angle, refractivity, dry temperature, dry pressure, and dry geopotential height are within ±0.02%/5 yrs, ±0.02%/5 yrs, ±0.06 K/5 yrs, ±0.02%/5 yrs, and ±2.3 m/5 yrs, respectively. Although the RO‐derived variables are not readily traceable to the international system of units, the high precision nature of the raw RO observables is preserved in the inversion chain.

Global Navigation Satellite System (GNSS) Receiver for Atmospheric Sounding (GRAS) is a radio occupation instrument especially designed and built for operational meteorological missions. GRAS has been developed by the European Space Agency (ESA) and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) in the framework of the EUMETSAT Polar System (EPS). The GRAS instrument is already flying on board the first MetOp satellite (.MetOp-A) that was launched in October 2006. It will also be on board two other MetOp satellites (MetOp-B and MetOp-C) that will successively cover the total EPS mission lifetime of over 14 yr. GRAS provides daily about 600 globally distributed occultation measurements and the GRAS data products are disseminated to the users in near-real time (NRT) so that they can be assimilated into numerical weather prediction (NWP) systems. All GRAS data and products are permanently archived and made available to the users for climate applications and scientific research through the EUMETSAT Unified Meteorological Archive and Retrieval Facility (U-MARF) and the GRAS Meteorology Satellite Application Facility (SAF) Archive and Retrieval Facility (GARF). The GRAS navigation data can be used in space weather applications.

Global Positioning System (GPS) radio occultation (RO) has provided continuous observations of the Earth's atmosphere since 2001 with global coverage, all-weather capability, and high accuracy and vertical resolution in the upper troposphere and lower stratosphere (UTLS). Precise time measurements enable long-term stability but careful processing is needed. Here we provide climate-oriented atmospheric scientists with multicenter-based results on the long-term stability of RO climatological fields for trend studies. We quantify the structural uncertainty of atmospheric trends estimated from the RO record, which arises from current processing schemes of six international RO processing centers, DMI Copenhagen, EUM Darmstadt, GFZ Potsdam, JPL Pasadena, UCAR Boulder, and WEGC Graz. Monthly-mean zonal-mean fields of bending angle, refractivity, dry pressure, dry geopotential height, and dry temperature from the CHAMP mission are compared for September 2001 to September 2008. We find that structural uncertainty is lowest in the tropics and mid-latitudes (50° S to 50° N) from 8 km to 25 km for all inspected RO variables. In this region, the structural uncertainty in trends over 7 yr is <0.03% for bending angle, refractivity, and pressure, <3 m for geopotential height of pressure levels, and <0.06 K for temperature; low enough for detecting a climate change signal within about a decade. Larger structural uncertainty above about 25 km and at high latitudes is attributable to differences in the processing schemes, which undergo continuous improvements. Though current use of RO for reliable climate trend assessment is bound to 50° S to 50° N, our results show that quality, consistency, and reproducibility are favorable in the UTLS for the establishment of a climate benchmark record.