Passive Microwave Rain Retrievals Using a New, Observations-Based, Parameterization of Sub-satellite Rain Variability
S. M. Hristova-Veleva, JPL, Pasadena, CA; and P. S. Callahan, R. S. Dunbar, B. W. Stiles, S. H. Yueh, J. N. Huddleston, S. V. Hsiao, G. Neumann, M. H. Freilich, B. A. Vanhoff, W. -. Y. Tsai, R. W. Gaston, E. Rodriguez, and D. E. Weissman
We have developed an algorithm to estimate atmospheric parameters (in particular rain) from microwave brightness temperatures to provide atmospheric correction for scatterometer wind retrieval from the SeaWinds scatterometer onboard the short-lived Midori-II satellite. This algorithm uses Advanced Microwave Scanning Radiometer (AMSR) observations but can be easily tailored to any other microwave radiometer. The scatterometer retrieval improvements in rain have led to a more general understanding of the potential of active/passive measurement of both precipitation and the underlying surface winds. This should have impact on the design of future missions. Multi-sensor retrievals of co-incident oceanic and atmospheric parameters improve our understanding of the sea-air fluxes and are of significant interest for global climate studies as well as studies of extreme weather events such as hurricanes.
In spite of its importance, precipitation is one of the most difficult elements of weather to measure. Satellite remote sensing techniques have shown considerable promise for deriving rain estimates on a global scale. Unfortunately, even today, we do not have an accurate estimate of precipitation. After evaluating the performance of 20 satellite precipitation algorithms, Smith et al. (1998) reported that the bias uncertainty of many algorithms is on the order of 30%. Even more disconcerting is the fact that instantaneous rain retrievals could differ by a factor of 2. Smith et al. (1998) pointed out that many of the discrepancies in the rainfall estimates come from differences in the assumed by the algorithms spatial inhomogeneity inside the sensor's Field of View (FOV) and differences in the assumed rain distributions, depending on whether they came from limited observations, cloud model simulations or conceptual models of hydrometeor structures.
The critical assumptions that affect rain retrievals from microwave observations are: i) beam filling (horizontal inhomogeneity within the sensor's FOV; ii) vertical rain profiles (vertical variability); iii) cloud vs rain partitioning; iv) assumptions about the particle size distributions; v) assumptions about the state of the underlying surface. All of these parameters vary significantly as a function of rain type (convective versus stratiform), geographical location (midlatitude versus tropical) and season. As Kummerow et al. (2004) pointed out, rainfall inhomogeneity varies both regionally and temporally and is not a simple function of rain rate. They stressed that “quantitative global statistics of rainfall inhomogeneity at scales below current FOV sizes are needed in order to asses the actual impact of this uncertainty on rainfall products”.
The precipitation variability must be reflected in the retrieval databases used by the algorithms and must be detectable during the point-by-point retrievals in order to allow for the selection and use of the subset of retrieval databases that are most suitable for a particular observational scene. As Panegrossi et al. (1998) pointed out, it is important to identify the typology of an observed event and to associate it with the appropriate retrieval database, generated from hydrometeor profiles that adequately represent the microphysical, macrophysical and environmental conditions of the storm.
We have developed a passive microwave retrieval algorithm with the corresponding retrieval databases containing the parameters of interest (e. g. rain rate) and the associated observables (e. g. microwave brightness temperatures) that are obtained from physical descriptions of the atmosphere, and the underlying surface, within a radiative transfer model. Our algorithm addresses in a new way the issues of non-uniform beam filling and hydrometeor uncertainty by using a number of retrieval databases, each capturing a mode of the natural variability of rain, as determined from existing observations. The novelty of the algorithm is in its ability to select the retrieval database that is most appropriate for a particular observational scene by using a specially developed Rain Indicator to determine the intensity and degree of homogeneity of the rain within the sensor's FOV. With this information in hand, it then selects the appropriate retrieval database for each observational scene to estimate a number of geophysical parameters, including vertically integrated liquid water and rain rate. All of the retrieval databases were built based on a large number of globally observed atmospheric structures. This makes the algorithm more suitable for global precipitation retrievals than algorithms that were developed based on cloud model simulations of squall lines and tropical convection. Another advantage of the algorithm is that in non-rainy conditions it retrieves sea surface temperature, near-surface wind speed, columnar vapor and cloud liquid water, while in rainy conditions it uses neighboring non-rainy estimates to specify the surface and vapor conditions under which the retrieval of precipitation should take place.
This presentation will have the following objectives: i) The first objective is to describe the basic features of the AMSR-based retrieval algorithm, focusing on the design and use of the two major algorithmic components: the rain indicator and the retrieval databases; ii) The second objective is to evaluate the performance of the algorithm through comparisons to retrievals based on other instruments. Comparisons between AMSR and NexRad rain rate retrievals will be emphasized; iii) Finally, the rain retrieval performance will be indirectly evaluated, through the impact that the rain correction has on the JPL scatterometer winds. Providing a good rain correction to scatterometer measurements is a very challenging problem. It requires good estimates of attenuation, precipitation backscatter and even estimates of the rain-induced roughening of the ocean surface. It is, thus, very sensitive to the accuracy of the rain rate estimates. Applying the AMSR-based atmospheric correction to the scatterometer observations has resulted in significant improvement of the scatterometer winds in rain (Hristova-Veleva et al., 2006; Stiles et al., 2006). Our current success shows the high potential of our AMSR-based geophysical retrieval algorithm and validates our approach.
Session 8, Remote Sensing
Thursday, 18 January 2007, 11:00 AM-12:30 PM, 207A
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