The Experimental Use of Multispectral and GPM Data in Hydrologic Applications in Alaska (297)

Aaron Jacobs, National Weather Service Forecast Office-Juneau, AK, Juneau, AK

Anita LeRoy, NASA SPoRT / University of Alabama in Huntsville-ESSC




A number of new weather satellites have launched recently, carrying instruments with improved resolutions, more spectral bands, and new capabilities, resulting in more data observations and a wide array of methods to combine and use them. This presents an opportunity for forecasters to experiment with using these new datasets together to improve operational forecasts. To prepare for the influx of new data, NWS-AJK, the APRFC, and NASA SPoRT have been assessing experimental datasets from NASA and NOAA missions over the past several years. One objective of these assessments has been to develop methods for operational hydrological applications in Alaska, where ground observations are relatively sparse and complex terrain and (lack of) coverage constrain the use of radar. Mountainous terrain, atmospheric river events, and a watershed complicated by glacial damming all create a variety of forecast challenges to test the effectiveness of new datasets.

In this study, we examined rain rate datasets from the NASA GPM mission, in conjunction with other datasets like multispectral RGB imagery from polar orbiting satellites that are proxies for GOES-16 data. Throughout the course of repeated assessments, we identified limitations that could be improved in future versions of the products, as well as strengths that we wished to see exploited to provide more effective guidance. The use of these datasets now, though not always routine in operations, has been greatly improved through these efforts.

Herein, we present some background on the types of GOES and GPM imagery and datasets that could be used in operational hydrology the process of acquiring, using, and incrementally improving experimental multispectral imagery and non-operational GPM data and the findings and case examples of operational &ldquobest practices&rdquo for use in the new era of satellite data.