Uncertainties of GPM Microwave Imager Precipitation Estimates Related to Precipitation System Size and Intensity

Abstract

The uncertainties in the version 5 Global Precipitation Measurement (GPM) Microwave Imager (GMI) precipitation retrievals are evaluated via comparison with the radar–radiometer (so-called “Combined”) retrievals between 40°S and 40°N. Results show the precipitation estimates are close (~7% GMI overestimation) globally. However, some specific regions, such as central Africa, the Amazon, the Himalayan region, and the tropical eastern Pacific, show a large overestimation (up to 50%) in GMI retrievals when compared to Combined retrievals. The uncertainties are further evaluated based on precipitation system properties, such as size and intensity of the system. GMI tends to underestimate precipitation volume when the system is relatively warm (>250 K) and small (<200 km2) due to the lack of ice scattering signatures. However, for large systems (>2000 km2), GMI-derived precipitation is typically higher than Combined over all surfaces. Based on the system properties, a simple bias correction methodology is proposed to implement in the Goddard Profiling Algorithm (GPROF) to reduce GMI biases. GMI precipitation volume is adjusted in each precipitation system based on the size and minimum 89 GHz polarization-corrected temperature (PCT) over land and ocean separately. The overall GMI bias is reduced to 3%, with significant improvement over land. The GMI biases (up to 50%) over the previously mentioned regions are significantly or partially removed, becoming less than 20%. This method also shows effectiveness in removing zonal and seasonal biases from GMI estimates. These results suggest the importance of utilizing the information of whole precipitation systems instead of individual pixels in the precipitation retrieval.

Differences in the Diurnal Variation of Precipitation Estimated by Spaceborne Radar, Passive Microwave Radiometer, and IMERG

“…Similar analysis using the PMW data set is given in Figure 1b. This PMW data set also captures the general precipitation pattern with the central Texas minimum (west of 100°W) and maximum in Mississippi/Alabama (approximately 90°–86°W), however, it is unable to capture the finer scale details such as the afore mentioned upslope precipitation, which may include strong warm rain processes that are difficult to detect using the PMW over land (Adhikari et al., 2019; Liu & Zipser, 2014; Nesbitt et al., 2004). The PMW data set also has a slight tendency to underestimate the precipitation recorded at the ground stations over most of the region, with an increased tendency along the Mississippi River (approximately 92°W–89°W) where differences can be as high as 1–2 mm/day.…”

“…Local time, geographic center location, total volumetric precipitation from radar and PMW, depth indicated by maximum height of detectable radar echo top, and size are calculated for each RTPF. Similar data sets have been used to understand the pros and cons of two precipitation retrievals in the past (Adhikari et al., 2019; Liu & Zipser, 2014; Nesbitt et al., 2004). In this study, precipitation contributions from RTPFs of different sizes and depths are calculated by accumulating the volumetric precipitation from RTPFs at each local time bin from either radar or PMW separately over the region of interest.…”

“…As was noted in the regional analysis, the Ku observations are somewhat noisy, especially compared to the IMERG data at this high (0.1 ° × 0.1 °) resolution. There are large differences between Ku and PMW over several specific regions, such as the eastern Pacific (e.g., ∼100° −90°W, ∼5°N), eastern Tibetan plateau, that are due to the detection differences by the two instruments (Adhikari et al, 2019;Liu & Zipser, 2014). IMERG has the highest precipitation rates throughout the entire domain, especially in the regions with the largest amounts of precipitation, such as the Inter-Tropical Convergence Zone (ITCZ) and HAYDEN AND LIU 10.1029/2020JD033020 13 of 24 over the Maritime Continent.…”

Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate

“…Hydrologists, for example, need accurate estimates of the rain volume at the catchment scale, while the NWP community places more value in the correct rain location and type than to retrieving the correct amount. In this direction, an interesting and novel approach is the quantification of the uncertainties of GMI precipitation retrievals based on precipitation system properties, such as the size and intensity of the system, which helps in removing zonal and seasonal biases, thus confirming the importance of using the information on the whole precipitation systems instead of individual pixels in the precipitation retrieval [258].…”

Assessment of Satellite Precipitation Products in Relation With Orographic Enhancement Over the Western United States

“…The primary types of sensors used to estimate precipitation from satellite measurements are radar, passive microwave imager/sounder, and Infrared radiometers. The space‐borne radar onboard satellites such as the Tropical Rainfall Measuring Mission (TRMM; Kummerow et al., 1998) and the Global Precipitation Measurement (GPM; Hou et al., 2014; Skofronick‐Jackson et al., 2018) have shown success in measuring precipitations globally, but they still suffer from uncertainties especially in mountainous regions (Adhikari et al., 2019). One major source of uncertainty associated with space‐borne radars is the contamination of near‐surface reflectivity profiles due to ground‐clutter (Arulraj & Barros, 2019; Liao et al., 2014).…”