Revealing the Winds under the Rain. Part I: Passive Microwave Rain Retrievals Using a New Observation-Based Parameterization of Subsatellite Rain Variability and Intensity—Algorithm Description

Abstract

Scatterometer ocean surface winds have been providing very valuable information to researchers and operational weather forecasters for over 10 years. However, the scatterometer wind retrievals are compromised when rain is present. Merely flagging all rain-affected areas removes the most dynamic and interesting areas from the wind analysis. Fortunately, the Advanced Earth Observing Satellite II (ADEOS-II) mission carried a radiometer [the Advanced Microwave Scanning Radiometer (AMSR)] and a scatterometer, allowing for independent, collocated retrievals of rain. The authors developed an algorithm that uses AMSR observations to estimate the rain inside the scatterometer beam. This is the first in a series of papers that describe their approach to providing rain estimation and correction to scatterometer observations. This paper describes the retrieval algorithm and evaluates it using simulated data. Part II will present its validation when applied to AMSR observations. This passive microwave rain retrieval algorithm addresses the issues of nonuniform beam filling and hydrometeor uncertainty in a novel way by 1) using a large number of soundings to develop the retrieval database, thus accounting for the geographically varying atmospheric parameters; 2) addressing the spatial inhomogeneity of rain by developing multiple retrieval databases with different built-in inhomogeneity and rain intensity, along with a ''rain indicator'' to select the most appropriate database for each observed scene; 3) developing a new cloud-versus-rain partitioning that allows the use of a variety of drop size distribution assumptions to account for some of the natural variability diagnosed from the soundings; and 4) retrieving atmospheric and surface parameters just outside the rainy areas, thus providing information about the environment to help decrease the uncertainty of the rain estimates.

Circumventing rain‐related errors in scatterometer wind observations

“…Ku band (13.4 GHz) scatterometers like QuikSCAT suffer more from rain contamination than C band (5.3 GHz) scatterometers such as ASCAT [ Bentamy et al , ], especially at low wind speeds [ Chelton et al , ]. Mitigation strategies are a topic of current research [ Weissman et al , ; Portabella et al , ; Bentamy et al , ; Hristova‐Veleva et al , ; Lin et al , , , ].…”

An Eye on the Storm: Integrating a Wealth of Data for Quickly Advancing the Physical Understanding and Forecasting of Tropical Cyclones

“…Its development was driven by the goal to enable investigations of the multiscale interactions that sometimes lead to the development of storms and other times do not, and to facilitate model evaluation (e.g. Hristova-Veleva et al 2012b) that goes beyond the comparison of "best track" metrics.…”

“…The Tropical Cyclone Data Archive (TCDA; https://tcis.jpl.nasa.gov/data/ TC_Data_Archive/) offers both digital data and imagery that are subset to the domain and time of interest, thus greatly reducing the volume of unwanted data. This makes the TCDA a valuable source to quickly build statistics in support of research, forecast improvement, and algorithm development (e.g., Hristova-Veleva et al 2014;Wu et al 2012).…”

Long-Term Comparison of Collocated Instantaneous Rain Retrievals from the TRMM Microwave Imager and Precipitation Radar over the Ocean

“…In general, convective regions are characterized by higher spatial variability, and hence the retrieval of convective rain will be associated with more uncertainty related to the degree of homogeneity inside the satellite FOV (e.g., Hristova-Veleva et al 2013). The V7 algorithm uses an a priori database that contains all cloud-radiation relations regardless of the classifications by rain presence and rain type.…”