Near-Real-Time Surface-Based CAPE from Merged Hyperspectral IR Satellite Sounder and Surface Meteorological Station Data

Author:

Bloch Callyn1,Knuteson Robert O.1,Gambacorta Antonia2,Nalli Nicholas R.2,Gartzke Jessica1,Zhou Lihang3

Affiliation:

1. Cooperative Institute for Meteorological Satellite Studies, Space Science and Engineering Center, University of Wisconsin–Madison, Madison, Wisconsin

2. I.M. Systems Group, Inc., Rockville, and NOAA/NESDIS/Center for Satellite Applications and Research, College Park, Maryland

3. NOAA/NESDIS/Center for Satellite Applications and Research, College Park, Maryland

Abstract

AbstractNear-real-time satellite-derived temperature and moisture soundings provide information about the changing atmospheric vertical thermodynamic structure occurring between successive routine National Weather Service (NWS) radiosonde launches. In particular, polar-orbiting satellite soundings become critical to the computation of stability indices over the central United States in the midafternoon, when there are no operational NWS radiosonde launches. Accurate measurements of surface temperature and dewpoint temperature are key in the calculation of severe weather indices, including surface-based convective available potential energy (SBCAPE). This paper addresses a shortcoming of current operational infrared-based satellite soundings, which underestimate the surface parcel temperature and dewpoint when CAPE is nonzero. This leads to a systematic underestimate of SBCAPE. This paper demonstrates a merging of satellite-derived vertical profiles with surface observations to address this deficiency for near-real-time applications. The National Oceanic and Atmospheric Administration (NOAA) Center for Environmental Prediction (NCEP) Meteorological Assimilation Data Ingest System (MADIS) hourly surface observation data are blended with satellite soundings derived using the NOAA Unique Combined Atmospheric Processing System (NUCAPS) to create a greatly improved SBCAPE calculation. This study is not intended to validate NUCAPS or the combined NUCAPS + MADIS product, but to demonstrate the benefits of combining observational weather satellite profile data and surface observations. Two case studies, 18 June 2017 and 3 July 2017, are used in this study to illustrate the success of the combined NUCAPS + MADIS SBCAPE compared to the NUCAPS-only SBCAPE estimate. In addition, a 6-month period, April–September 2018, was analyzed to provide a comprehensive analysis of the impact of using surface observations in satellite SBCAPE calculations. To address the need for reduced data latency, a near-real-time merged satellite and surface observation product is demonstrated using NUCAPS products from the Community Satellite Processing Package (CSPP) applied to direct broadcast data received at the University of Wisconsin–Madison, Hampton University in Virginia, and the Naval Research Laboratory in Monterey, California. Through this study, it is found that the combination of the MADIS surface observation data and the NUCAPS satellite profile data improves the SBCAPE estimate relative to comparisons with the Storm Prediction Center (SPC) mesoscale analysis and the NAM analysis compared to the NUCAPS-only SBCAPE estimate. An assessment of the 6-month period between April and September 2018 determined the dry bias in NUCAPS at the surface is the primary cause of the underestimation of the NUCAPS-only SBCAPE estimate.

Publisher

American Meteorological Society

Subject

Atmospheric Science

Reference41 articles.

1. Assessing the vertical distribution of convective available potential energy;Blanchard;Wea. Forecasting,1998

2. SHARPpy: An open source sounding analysis toolkit for the atmospheric sciences;Blumberg;Bull. Amer. Meteor. Soc.,2017

3. Bowlan, M., and K.Calhoun, 2018: GOES-R and JPSS Proving Ground Demonstration at the Hazardous Weather Testbed 2018 Spring Experiment Final Evaluation. GOES-R/JPSS Proving Ground Demonstrations Documentation, 59 pp., https://www.goes-r.gov/users/docs/pg-activities/HWT2018_SE_GOESRPG_Final_Report.pdf.

4. An empirical model for assessing the severe weather potential of developing convection;Cintineo;Wea. Forecasting,2014

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