Limin Zhao

NOAA operational hydrological products derived from the advanced microwave sounding unit

With the launch of the NOAA-15 satellite in May 1998, a new generation of passive microwave sounders was initiated. The Advanced Microwave Sounding Unit (AMSU), with 20 channels spanning the frequency range from 23-183 GHz, offers enhanced temperature and moisture sounding capability well beyond its predecessor, the Microwave Sounding Unit (MSU). In addition, by utilizing a number of window channels on the AMSU, the National Oceanic and Atmospheric Administration (NOAA) expanded the capability of the AMSU beyond this original purpose and developed a new suite of products that are generated through the Microwave Surface and Precipitation Products System (MSPPS). This includes precipitation rate, total precipitable water, land surface emissivity, and snow cover. Details on the current status of the retrieval algorithms (as of September 2004) are presented. These products are complimentary to similar products obtained from the Defense Meteorological Satellite Program Special Sensor Microwave/Imager (SSMI) and the Earth Observing Aqua Advanced Microwave Scanning Radiometer (AMSR-E). Due to the close orbital equatorial crossing time between NOAA-16 and the Aqua satellites, comparisons between several of the MSPPS products are made with AMSR-E. Finally, several application examples are presented that demonstrate their importance to weather forecasting and analysis, and climate monitoring.

Retrieval of Ice Cloud Parameters Using the Advanced Microwave Sounding Unit

Abstract An algorithm is developed to derive cloud ice water path (IWP) and ice particle effective diameters De from the advanced microwave sounding unit (AMSU) measurements. In the algorithm, both IWP and De are related to the ice particle scattering parameters, which are determined from the AMSU 89- and 150-GHz measurements. The ratio of the scattering parameters measured at two frequencies provides a direct estimate of De. IWP is then derived from the scattering parameter at 150 GHz with the derived De and the constant bulk volume density. A screening procedure is developed to discriminate the scattering signatures between atmospheric clouds and surface materials. The major error sources affecting the retrievals are identified. The errors of retrieved effective diameter are primarily controlled by the errors in estimating cloud-base brightness temperatures at 89 and 150 GHz and the errors of the bulk volume density. It is shown that De possibly contains an error of 5%–20%. For the retrieval of cloud ic...

Precipitation characteristics over land from the NOAA-15 AMSU sensor

Window channel measurements from the NOAA-15 Advanced Microwave Sounding Unit (AMSU) are used to retrieve rain rates. This study focuses on the rain rate retrievals over land and uses measurements at 89 and 150 GHz to detect a scattering signal by millimeter-sized ice particles under precipitating atmospheres. An operational algorithm using the AMSU-A module (89 GHz) implemented in August 1999 is also described. The algorithms are calibrated with hourly surface rain observations. It is shown that identification of stratiform rain can be dramatically improved using measurements at 150 GHz from the AMSU-B sensor. Improved spatial resolution of AMSU-B is a major factor contributing to this algorithm.

The Improved AMSU Rain-Rate Algorithm and Its Evaluation for a Cool Season Event in the Western United States

Abstract Rain-rate retrievals from passive microwave sensors are useful for a number of applications related to weather forecasting. For example, in the United States, such estimates are useful for offshore rainfall systems approaching land and in regions where the Weather Surveillance Radar-1988 Doppler (WSR-88D) network is inadequate. Improvements have been made to the rain-rate retrieval from the Advanced Microwave Sounding Unit (AMSU) on board the National Oceanic and Atmospheric Administration (NOAA) Polar Orbiting Environmental Satellites (POESs). The new features of the improved rain-rate algorithm include a two-stream correction of the satellite brightness temperatures at 89 and 150 GHz, cloud- and rain-type classification for better retrieval of rain rate, and removal of the two ad hoc thresholds in the ice water path (IWP) and effective diameter (De) retrieval where the scattering signals are very small. In this paper, the new algorithm has been compared to the previous NOAA operational algorith...

NOAA satellite‐derived hydrological products prove their worth

Satellite observations are particularly important for monitoring the global changes of atmospheric and surface features. For many parameters, satellite measurements are the only means of obtaining this information, particularly over the oceans and sparsely-populated land areas. For example, multi-spectral measurements from both geostationary and polar-orbiting satellites are key components of the Global Precipitation Climatology Project (GPCP) [Huffman et al., 1996], which has measured global rainfall for over 20 years. In addition, the longstanding National Oceanic and Atmospheric Administration (NOAA)-based Northern Hemispheric snow cover climatology has relied almost solely on satellite observations that are interpreted by satellite analysts [Robinson et al., 1993].

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

Snowfall rate retrieval from space-borne 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 (AMSU-A)/Microwave Humidity Sounder (MHS) sensor pair and the Advanced Technology Microwave Sounder (ATMS). 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 (SD) algorithm that is composed of a satellite-based statistical model and a set of numerical weather prediction (NWP) model-based filters. There are four components in the SFR algorithm itself: cloud properties retrieval, computation of ice particle terminal velocity, ice water content (IWC) 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 towards 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 (NOAA) and can be obtained from that organization.