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Case, Jonathan L.

Publications and source records attributed to Case, Jonathan L..

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A Nowcasting Approach for Low-Earth-Orbiting Hyperspectral Infrared Soundings within the Convective Environment

Low-Earth-orbiting (LEO) hyperspectral infrared (IR) sounders have significant yet untapped potential for characterizing thermodynamic environments of convective initiation and ongoing convection. While LEO soundings are of value to weather forecasters, the temporal resolution needed to resolve the rapidly evolving thermodynamics of the convective environment is limited. Here, we have developed a novel nowcasting methodology to extend snapshots of LEO soundings forward in time up to 6 h to create a product available within National Weather Service systems for user assessment. Our methodology is based on parcel forward-trajectory calculations from the satellite-observing time to generate future soundings of temperature (T) and specific humidity (q) at regularly gridded intervals in space and time. The soundings are based on NOAA-Unique Combined Atmospheric Processing System (NUCAPS) retrievals from the Suomi National Polar-Orbiting Partnership (Suomi NPP) and NOAA-20 satellite platforms. The tendencies of derived convective available potential energy (CAPE) and convective inhibition (CIN) are evaluated against gridded, hourly accumulated rainfall obtained from the Multi-Radar Multi-Sensor (MRMS) observations for 24 hand-selected cases over the contiguous United States. Areas with forecast increases in CAPE (reduced CIN) are shown to be associated with areas of precipitation. The increases in CAPE and decreases in CIN are largest for areas that have the heaviest precipitation and are statistically significant compared to areas without precipitation. These results imply that adiabatic parcel advection of LEO satellite sounding snapshots forward in time are capable of identifying convective initiation over an expanded temporal scale compared to soundings used only during the LEO satellite overpass time.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2020

Verification of the Convection-Allowing Ensemble System over the Hindu Kush Himalaya Region During the 2018 and 2019 Pre-Monsoon Severe Thunderstorm Seasons

Some of the most intense thunderstorms on the planet occur in the Hindu Kush Himalaya (HKH) region of South-Central Asia. NASA/SERVIR Applied Sciences Team competitive project to develop capacity of severe thunderstorm monitoring and forecasting tool for HKH. Project Goal: Use [NASA] modeling and remote-sensing assets to build early warning capabilities and facilitate timely disaster response for high impact weather events in the HKH region. Specific objectives: 1. Prototype and transition High-Impact Weather Assessment Toolkit (HIWAT) 2. Jointly develop HIWAT capabilities & training with SERVIR’s hub in Kathmandu, Nepal: International Centre for Integrated Mountain Development (ICIMOD) 3. Demonstrate capacity in end-user environment 4. Transition HIWAT system to ICIMOD for future maintenance.

Case, Jonathan L.↗
NASA NTRS2019

The WRF Lightning Forecasting Algorithm: Sensitivities to Microphysics and Boundary Layer Physics Schemes

As with WRF model simulated convection in general, LFA output is sensitive to cloud and PBL physics. WRF convection is also sensitive to model initialization uncertainties and unresolved errors.  Large sensitivity exists in HRRR-like Thompson microphysics and PBL, for which original LFA calibration constant needs to be multiplied by 1.13 to give proper FRD amplitudes. Thompson microphysics scheme also shows poorest correlations relative to reference data, suggesting low predictability of HRRR LFA output. Need to validate HRRR LFA against GLM observations.

weather prediction↗
NASA NTRS2019

Using Machine Learning to Develop a Predictive Model for Future Fire Seasons

The deep learning model shows promise for predicting areas of high wildfire potential. Full evaluation of the model performance is ongoing. Currently, the developed deep learning model is better overall at predicting the number of fires over the acres burned. Acres burned is dependent on location, suppression plan, and current conditions. Antecedent conditions are only one piece of the equation. In-season changes are not accounted for. An ignition source is required, which further complicates the model training and prediction.

White, Andrew T.↗
NASA NTRS2019

Assimilation of GPM Retrieved Surface Meteorology Variables with ICE-POP Case Studies

Built upon Tropical Rainfall Measuring Mission (TRMM) legacy for next-generation global observation of rain and snow. The GPM has a broad global coverage ~70°S – 70°N with a swath of 245/125-km for the Ka (35.5 GHz)/Ku (13.6 GHz) band radar, and 850-km for the 13-channel GMI. GPM also features better retrievals for heavy, moderate, and light rain and snowfall.

GPM retrieval product↗