The Feasibility of Retrieving Drizzle Drop Size Information from Satellites Using a Multi-sensor Optimal Estimation Algorithm
Abstract
Over the high latitude oceans, where broad areas of light precipitation are common, significant disagreements exist between satellite-based precipitation estimates. Precipitation retrieval algorithms for both active and passive instruments rely on uncertain and simplistic assumptions regarding drop size distributions (DSDs), due in part to the fact that no single sensor gives enough information to be able to reliably determine the DSD shape. Given that changes to the DSD can have a large impact on surface precipitation rate, it is desirable to be able to retrieve, as a set of continuously varying parameters, the DSD that is most consistent with available observations and a-priori information, as opposed to assuming a blanket DSD. To that end, in this study we explore whether coincident observations from multiple satellite instruments, when combined in an optimal estimation framework, together contain enough independent information to meaningfully characterize the DSD. Sample oceanic DSDs, obtained from ship-based disdrometers that are part of the Ocean Rainfall and Ice-phase Precipitation Measurement Network (OceanRAIN), are used to simulate a set of multi-sensor observations for each DSD: reflectivity profiles for the GPM Dual-frequency Precipitation Radar and the CloudSat Cloud Profiling Radar, brightness temperatures for all channels of the GPM Microwave Imager, and a visible optical depth measurement for the Moderate Resolution Imaging Spectroradiometer. From these simulated observations, we attempt to retrieve three parameters describing the shape of the DSD, assuming a modified gamma distribution. We first assume an idealized scenario, with matching instrument footprints, no field-of-view inhomogeneity, and no ice-phase particles, among other simplifications. We then gradually introduce more realistic complications, in order to determine the impact of each assumption. Implications for multi-sensor retrievals using actual observations from CloudSat and the GPM Core Observatory are discussed.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFM.H53C..08S
- Keywords:
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- 3354 Precipitation;
- ATMOSPHERIC PROCESSESDE: 3360 Remote sensing;
- ATMOSPHERIC PROCESSESDE: 1817 Extreme events;
- HYDROLOGYDE: 1847 Modeling;
- HYDROLOGY