Assimilating synthetic hyperspectral sounder temperature and humidity retrievals to improve severe weather forecasts

Atmospheric spectra reconstructed from interferometric measurements are susceptible to scene motion, which can be caused by changing environment or instrument jitter. This leads to a coupling between the time series of the modulated scene radiance and the interferogram collected as a function of time. Spectral artifacts are generated when this occurs and appear as both narrow, isolated spikes in the measurement wavelength band as well as broad, out-of-band features. Here, we derive an analytical relationship that depends on jitter frequency, amplitude and phase, and the scene gradient. The observed radiance is expressed as a spatially weighted mixture of radiance originating from various points within the scene. Spectral artifacts created by relative scene motion are shown to be proportional to the radiometric scene difference within the instrument field of view (FOV) and jitter amplitude, and vary significantly with the jitter frequency. The analytical solution can be useful in various applications, where image instability is expected to have an effect on measurements in question, ranging from spectroscopy of turbine plumes, through airborne measurements of smoke density and chemical composition in natural fire or volcanic events, to atmospheric sounding from space.

Effects of line-of-sight motion on hyperspectral Fourier transform measurements

Global Navigation Satellite System (GNSS) radio occultations (RO) over the last 10 years have proved to be a valuable and essentially unbiased data source for operational global numerical weather prediction. However, the existing sampling coverage is too sparse in both space and time to support forecasting of severe mesoscale weather. In this study, the case study or quick observing system simulation experiment (QuickOSSE) framework is used to quantify the impact of vastly increased numbers of GNSS RO profiles on mesoscale weather analysis and forecasting. The current study focuses on a severe convective weather event that produced both a tornado and flash flooding in Oklahoma on 31 May 2013. The WRF Model is used to compute a realistic and faithful depiction of reality. This 2-km “nature run” (NR) serves as the “truth” in this study. The NR is sampled by two proposed constellations of GNSS RO receivers that would produce 250 thousand and 2.5 million profiles per day globally. These data are then ...

https://journals.ametsoc.org/doi/pdf/10.1175/MWR-D-16-0212.1

A Severe Weather Quick Observing System Simulation Experiment (QuickOSSE) of Global Navigation Satellite System (GNSS) Radio Occultation (RO) Superconstellations

As operational forecast and data assimilation (DA) systems evolve, observing system simulation experiment (OSSE) systems must evolve in parallel. Expected development of operational systems—especially the use of data that are currently not used or are just beginning to be used, such as all-sky and surface-affected microwave radiances—will greatly challenge our ability to construct realistic OSSE systems. An additional set of challenges will arise when future DA systems strongly couple the different Earth system components. In response, future OSSE systems will require coupled models to simulate nature and coupled observation simulators. The requirements for future evolving OSSE systems and potential solutions to satisfy these requirements are discussed. It is anticipated that in the future the OSSE technique will be applied to diverse and coupled domains with the use of increasingly advanced and sophisticated simulations of nature and observations.

Future Observing System Simulation Experiments

Several studies have shown that a geostationary hyperspectral imager/sounder can provide the most significant value increase in short term, regional numerical prediction weather models over a range of other options. In 1998, the Geostationary Imaging Fourier Transform Spectrometer (GIFTS) proposal was selected by NASA as the New Millennium Earth Observation 3 program over several other geostationary instrument development proposals. After the EO3 GIFTS flight demonstration program was changed to an Engineering Development Unit (EDU) due to funding limitations by one of the partners, the EDU was subjected to flight-like thermal vacuum calibration and testing and successfully validated the breakthrough technologies needed to make a successful observatory. After several government stops and starts, only EUMETSAT’s Meteosat Third Generation (MTG-S) sounder is in operational development. Recently, a commercial partnership has been formed to fill the significant data gap. AsiaSat has partnered with GeoMetWatch (GMW)1 to fund the development and launch of the Sounding and Tracking Observatory for Regional Meteorology (STORMTM) sensor, a derivative of the Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) EDU that was designed, built, and tested by Utah State University (USU). STORMTM combines advanced technologies to observe surface thermal properties, atmospheric weather, and chemistry variables in four dimensions to provide high vertical resolution temperature and moisture sounding information, with the fourth dimension (time) provided by the geosynchronous satellite platform ability to measure a location as often as desired. STORMTM will enhance the polar orbiting imaging and sounding measurements by providing: (1) a direct measure of moisture flux and altitude-resolved water vapor and cloud tracer winds throughout the troposphere, (2) an observation of the time varying atmospheric thermodynamics associated with storm system development, and (3) the transport of tropospheric pollutant gases. The AsiaSat/GMW partnership will host the first STORMTM sensor on their AsiaSat 9 telecommunications satellite at 122 E over the Asia Pacific area. GMW’s business plan is to sell the unique STORM data and data products to countries and companies in the satellite coverage area. GMW plans to place 6 STORMTM sensors on geostationary telecommunications satellites to provide global hyperspectral sounding and imaging data. Utah State University’s Advanced Weather Systems Laboratory (AWS) will build the sensors for GMW.

https://www.spiedigitallibrary.org/conference-proceedings-of-spie/8867/886702/Rapidly-updated-hyperspectral-sounding-and-imaging-data-for-severe-storm/10.1117/12.2030687.pdf

Rapidly updated hyperspectral sounding and imaging data for severe storm prediction

Ultra-spectral sounders (USS) in low earth orbit have significantly improved weather forecast accuracy in recent years, and their impact could be significantly improved with reduced revisit times. The GeoMetWatch, Inc. 1 Sounding and Tracking Observatory for Regional Meteorology (STORM TM ) program is designed to place a constellation of six USS units in spaced geostationary (GEO) positions around the earth. From GEO, the repeat time for a specific weather feature can be reduced to minutes, and the vertical temperature, water vapor and wind profiles can provide detailed warnings not available by any other means. The STORM TM sensor, a derivative of the Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) EDU that was designed and built for NASA by Utah State University (USU) and rigorously tested in 2006, will be launched on a commercial geostationary satellite in late 2016. It combines advanced technologies to provide improved performance and reliability over the original EDU. From GEO the USS can observe surface thermal properties and atmospheric weather and chemistry variables in four dimensions. This paper provides an overview of the STORM TM instrument and the measurement concept. STORM TM ’s USS will provide data of the same quality as the current LEO satellite sounders (AIRS, CrIS, and IASI) but with the ability to track storm development with soundings and images at any desired rate. Wind profiles obtained from a time sequence of STORM TM water vapor retrieval images will provide additional input to now casting and regional models.

/pdf/storm-sounding-and-tracking-observatory-for-regional-4dhhm1sjad.pdf

STORM: sounding and tracking observatory for regional meteorology to launch in 2016

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Rapidly updated hyperspectral sounding and imaging data for severe storm prediction

STORM: sounding and tracking observatory for regional meteorology to launch in 2016