Recursive Filter Objective Analysis of Meteorological Fields: Applications to NESDIS Operational Processing

Evaluating the Ability of Remote Sensing Observations to Identify Significantly Severe and Potentially Tornadic Storms

Remote sensing observations, especially those from ground-based radars, have been used extensively to discriminate between severe and nonsevere storms. Recent upgrades to operational remote sensing networks in the United States have provided unprecedented spatial and temporal sampling to study such storms. These networks help forecasters subjectively identify storms capable of producing severe weather at the ground; however, uncertainties remain in how to objectively identify severe thunderstorms using the same data. Here, three large-area datasets (geostationary satellite, ground-based radar, and ground-based lightning detection) are used over 28 recent events in an attempt to objectively discriminate between severe and nonsevere storms, with an additional focus on severe storms that produce tornadoes. Among these datasets, radar observations, specifically those at mid- and upper levels (altitudes at and above 4 km), are shown to provide the greatest objective discrimination. Physical and kinematic storm characteristics from all analyzed datasets imply that significantly severe [≥2-in. (5.08 cm) hail and/or ≥65-kt (33.4 ms-1) straight-line winds] and tornadic storms have stronger upward motion and rotation than nonsevere and less severe storms. In addition, these metrics are greatest in tornadic storms during the time in which tornadoes occur.

Demonstration and Evaluation of 3D Winds Generated by Tracking Features in Moisture and Ozone Fields Derived from AIRS Sounding Retrievals

For more than 15 years, polar winds from the Moderate Resolution Imaging Spectroradiometer (MODIS) imagery have been generated by the National Oceanic and Atmospheric Administration (NOAA) and the Cooperative Institute for Meteorological Satellite Studies (CIMSS). These datasets are a NOAA National Environmental Satellite, Data, and Information Service (NESDIS) operational satellite product that is used at more than 10 major numerical weather prediction (NWP) centers worldwide. The MODIS polar winds product is composed of both infrared window (IR-W) and water vapor (WV) tracked features. The WV atmospheric motion vectors (AMV) yield a better spatial distribution than the IR-W since both cloud and clear-sky features can be tracked in the WV images. As the new generation polar satellite-era begins with the Suomi National Polar-orbiting Partnership (S-NPP), there is currently no WV channel on the Visible/Infrared Imager/Radiometer Suite (VIIRS), resulting in a data gap with only IR-W derived AMVs possible. This scenario presents itself as an opportunity to evaluate hyperspectral infrared moisture retrievals from consecutive overlapping satellite polar passes to extract atmospheric motion from clear-sky regions on constant (and known) pressure surfaces, i.e., estimating winds in retrieval space rather than radiance space. Perhaps most significantly, this method has the potential to provide vertical wind profiles, as opposed to the current MODIS-derived single-level AMVs. In this study, the winds technique is applied to Atmospheric Infrared Sounder (AIRS) moisture retrievals from NASA’s Aqua satellite. The resulting winds are assimilated into the Goddard Earth Observing System Model, Version 5 (GEOS-5). The results are encouraging, as the AIRS retrieval polar AMVs have a similar quality as the MODIS AMVs and exhibit a positive impact in the hemispheric Day 4.5 to 6.5 forecasts for a one-month experiment in July 2012.