A 1DVAR‐based snowfall rate retrieval algorithm for passive microwave radiometers

Abstract

Snowfall rate retrieval from spaceborne passive microwave (PMW) radiometers has gained momentum in recent years. PMW can be so utilized because of its ability to sense in‐cloud precipitation. A physically based, overland snowfall rate (SFR) algorithm has been developed using measurements from the Advanced Microwave Sounding Unit‐A/Microwave Humidity Sounder sensor pair and the Advanced Technology Microwave Sounder. Currently, these instruments are aboard five polar‐orbiting satellites, namely, NOAA‐18, NOAA‐19, Metop‐A, Metop‐B, and Suomi‐NPP. The SFR algorithm relies on a separate snowfall detection algorithm that is composed of a satellite‐based statistical model and a set of numerical weather prediction model‐based filters. There are four components in the SFR algorithm itself: cloud properties retrieval, computation of ice particle terminal velocity, ice water content adjustment, and the determination of snowfall rate. The retrieval of cloud properties is the foundation of the algorithm and is accomplished using a one‐dimensional variational (1DVAR) model. An existing model is adopted to derive ice particle terminal velocity. Since no measurement of cloud ice distribution is available when SFR is retrieved in near real time, such distribution is implicitly assumed by deriving an empirical function that adjusts retrieved SFR toward radar snowfall estimates. Finally, SFR is determined numerically from a complex integral. The algorithm has been validated against both radar and ground observations of snowfall events from the contiguous United States with satisfactory results. Currently, the SFR product is operationally generated at the National Oceanic and Atmospheric Administration and can be obtained from that organization.

Time‐Lag Correlation Between Passive Microwave Measurements and Surface Precipitation and Its Impact on Precipitation Retrieval Evaluation

“…They related this observation to the time it takes for the raindrops to reach the gauge from the time they are observed by the spaceborne sensors. Meng et al () also observed time lag in the satellite retrieved snowfall rate. They found that snowfall rates observed by the Advanced Technology Microwave Sounder (ATMS) correlated most strongly with the surface snowfall rate at a ∼30‐min lag.…”

The activities of the international precipitation working group

“…Of particular importance is the outstanding issue of snowfall (e.g. Ebtehaj & Kummerow, ; Kneifel, Kulie, & Bennartz, ; Kulie et al, ; Meng et al, ; You, Wang, Ferraro, & Rudlosky, ) and hail (e.g. Ferraro, Beauchamp, Cecil, & Heymsfield, ; Mroz et al, ) estimation as well as the improvement of the retrieval of low‐intensity rainfall (Behrangi, Tian, Lambrigtsen, & Stephens, ) and the correction for orographic effects (Shige, Kida, Ashiwake, Kubota, & Aonashi, ; Yamamoto & Shige, ; Yamamoto, Shige, Yu, & Chen, ).…”

A hybrid snowfall detection method from satellite passive microwave measurements and global forecast weather models

“…An atmospheric level is considered to have cloud if relative humidity is at or above 89%. This threshold is consistent with the critical relative humidity value used in the GFS microphysics scheme for one‐degree resolution at midlatitude locations (Meng et al, ).…”

“…They also found that brightness temperatures at ATMS high-frequency channels are significantly higher for snowfall, but only in FIGURE 2 (from Laviola et al, 2015). Left: new ATMS SD algorithm, and right: inversion of Radiative Transfer (RT) module in the snowfall rate algorithm of Meng et al (2017). Limb-corrected channel 6 ATMS brightness temperature (TB53L) is used as proxy for surface temperature.…”