Passive Microwave Precipitation Detection Biases: Relationship to Cloud Properties

Accurate measurement of the Earth’s hydrologic cycle requires a more precise understanding of precipitation accumulation and intensity on a global scale. While there is a long record of passive microwave satellite measurements, passive microwave rainfall retrievals often fail to detect light precipitation or have light rain intensity biases because they cannot differentiate between emission from cloud and rain water. Previous studies have shown that AMSR-E significantly underestimates rainfall occurrence and volume compared to CloudSat. This underestimation totals just below 0.6 mm/day quasi-globally (60S-60N), but there are larger regional variations related to the dominant cloud regime. This study aims to use Moderate Resolution Imaging Spectroradiometer (MODIS) and the 94-GHz CloudSat Cloud Profiling Radar (CPR), which has a high sensitivity to light rain, with the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) observations, to help better characterize the properties of clouds that lead to passive microwave rainfall detection biases. CPR cloud and precipitation retrievals, AMSR-E Level-2B Goddard Profiling 2010 Algorithm (GPROF 2010) rainfall retrievals, and MODIS cloud properties were collocated and analyzed for 2007-2009. MODIS cloud microphysical and macrophysical properties, such as optical thickness, particle effective radius, and liquid water path were analyzed when precipitation is detected by CloudSat and missed by AMSR-E. Results are consistent with past studies and show large passive microwave precipitation detection biases compared to CloudSat in stratocumulus and shallow cumulus regimes. An examination of cases where AMSR-E failed to detect precipitation detected by CloudSat shows that warm rain detection biases occur more frequently within lower LWP, τ , and CTH bins, but biases at higher LWP, τ_ , and CTH contribute more to the total frequency of missed precipitation. Warm rain detection biases occur more frequently and biases contribute to more of the total frequency of missed precipitation for rve > 16 µ-m. Cloud property-dependent thresholds were calculated and compared against Advanced Microwave Scanning Radiometer (Earth Observing System) (AMSR-E) Goddard Profiling Algorithm (GPROF). All cloud property-dependent brightness temperature (TB) thresholds showed improvements in hit rate and volumetric hit rates. Cloud property-dependent TB thresholds were investigated to determine if thresholds can be improved by separately constraining data to environmental and cloud regimes. Descent and stratocumulus regimes, which generally consist of warm clouds, showed further improvements of warm rain detection. Results suggest that aprori knowledge of cloud property information and environmental information could significantly improve the detection of warm precipitation in GPROF retrievals

Exploring the limits of variational passive microwave retrievals

2017 Summer.Includes bibliographical references.Passive microwave observations from satellite platforms constitute one of the most important data records of the global observing system. Operational since the late 1970s, passive microwave data underpin climate records of precipitation, sea ice extent, water vapor, and more, and contribute significantly to numerical weather prediction via data assimilation. Detailed understanding of the observation errors in these data is key to maximizing their utility for research and operational applications alike. However, the treatment of observation errors in this data record has been lacking and somewhat divergent when considering the retrieval and data assimilation communities. In this study, some limits of passive microwave imager data are considered in light of more holistic treatment of observation errors. A variational retrieval, named the CSU 1DVAR, was developed for microwave imagers and applied to the GMI and AMSR2 sensors for ocean scenes. Via an innovative method to determine forward model error, this retrieval accounts for error covariances across all channels used in the iteration. This improves validation in more complex scenes such as high wind speed and persistently cloudy regimes. In addition, it validates on par with a benchmark dataset without any tuning to in-situ observations. The algorithm yields full posterior error diagnostics and its physical forward model is applicable to other sensors, pending intercalibration. This retrieval is used to explore the viability of retrieving parameters at the limits of the available information content from a typical microwave imager. Retrieval of warm rain, marginal sea ice, and falling snow are explored with the variational retrieval. Warm rain retrieval shows some promise, with greater sensitivity than operational GPM algorithms due to leveraging CloudSat data and accounting for drop size distribution variability. Marginal sea ice is also detected with greater sensitivity than a standard operational retrieval. These studies ultimately show that while a variational algorithm maximizes the effective signal to noise ratio of these observations, hard limitations exist due to the finite information content afforded by a typical microwave imager

A Physical Model to Estimate Snowfall over Land using AMSU-B Observations

In this study, we present an improved physical model to retrieve snowfall rate over land using brightness temperature observations from the National Oceanic and Atmospheric Administration's (NOAA) Advanced Microwave Sounder Unit-B (AMSU-B) at 89 GHz, 150 GHz, 183.3 +/- 1 GHz, 183.3 +/- 3 GHz, and 183.3 +/- 7 GHz. The retrieval model is applied to the New England blizzard of March 5, 2001 which deposited about 75 cm of snow over much of Vermont, New Hampshire, and northern New York. In this improved physical model, prior retrieval assumptions about snowflake shape, particle size distributions, environmental conditions, and optimization methodology have been updated. Here, single scattering parameters for snow particles are calculated with the Discrete-Dipole Approximation (DDA) method instead of assuming spherical shapes. Five different snow particle models (hexagonal columns, hexagonal plates, and three different kinds of aggregates) are considered. Snow particle size distributions are assumed to vary with air temperature and to follow aircraft measurements described by previous studies. Brightness temperatures at AMSU-B frequencies for the New England blizzard are calculated using these DDA calculated single scattering parameters and particle size distributions. The vertical profiles of pressure, temperature, relative humidity and hydrometeors are provided by MM5 model simulations. These profiles are treated as the a priori data base in the Bayesian retrieval algorithm. In algorithm applications to the blizzard data, calculated brightness temperatures associated with selected database profiles agree with AMSU-B observations to within about +/- 5 K at all five frequencies. Retrieved snowfall rates compare favorably with the near-concurrent National Weather Service (NWS) radar reflectivity measurements. The relationships between the NWS radar measured reflectivities Z(sub e) and retrieved snowfall rate R for a given snow particle model are derived by a histogram matching technique. All of these Z(sub e)-R relationships fall in the range of previously established Z(sub e)-R relationships for snowfall. This suggests that the current physical model developed in this study can reliably estimate the snowfall rate over land using the AMSU-B measured brightness temperatures