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Bryan M Karpowicz

Publications and source records attributed to Bryan M Karpowicz.

16 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

Impacts of an Early Morning Low Earth Orbit Observing Platform in A Future Global Observing Network Scenario

Significant changes to the global observing network are expected in the coming decades including the launch of a global ring of geostationary satellites and a reduction in the number of low earth orbit (LEO) platforms. It is anticipated that there may be a gap in the LEO coverage between the planned mid-morning and early afternoon orbits. Here, the utility of an early morning LEO orbit for numerical weather prediction is considered using an observing system simulation experiment (OSSE). A global observing network with two LEO platforms including microwave and hyperspectral infrared instruments and three geostationary hyperspectral infrared platforms is considered for a future baseline scenario. Two instruments, a microwave radiometer modeled on the Advanced Technology Microwave Sounder (ATMS) and a hyperspectral infrared radiometer modeled on the Cross-track Infrared Sounder (CrIS), are tested both individually and in conjunction on a new early morning orbit in addition to the future baseline scenario. The microwave instrument is found to have beneficial impacts for up to 4–7 days in the medium range forecast period with beneficial impacts for the infrared instrument for up to 3–5 days. Short-range forecast impacts estimated with Forecast Sensitivity Observation Impacts (FSOI) over the conterminous United States for the early morning orbit are somewhat weaker than for the same instruments in the early afternoon orbit due to the orbital passage being coincident with rawinsondes while the afternoon orbit is coincident with the minima of both rawinsondes and aircraft.

Nikki C Prive↗
NASA NTRS2022

Assessment of Geo-Kompsat-2A Atmospheric Motion Vector Data and Its Assimilation Impact in the GEOS Atmospheric Data Assimilation System

Korea’s second geostationary meteorological satellite, Geo-Kompsat-2A (Geostationary-Korean Multi-Purpose Satellite-2A, GK2A), was successfully launched on 4 December 2018. GK2Agenerates Atmospheric Motion Vectors (AMVs) every 10 min in the full disk area. This data hasbeen disseminated via Global Telecommunication System (GTS) since 25 October 2019. This articleevaluates the quality of GK2A AMVs in the Goddard Earth Observing System (GEOS) atmosphericdata assimilation system (ADAS). The data show slow wind speed biases at 200–300 hPa and 600–800hPa in the northern and southern hemispheres. These biases are caused by observation heightassignment errors near jet streams. The Equivalent Blackbody Temperature (EBBT) method of GK2Atends to assign clouds at higher altitude, which mainly causes slow wind speed biases, especially inthe lower atmosphere. The IR/WV intercept method of GK2A assigns clouds slightly lower in theatmospheric layers below the altitude of 400 hPa, which causes positive biases. Quality control (QC)criteria to select the most suitable GK2A AMV data for assimilation are presented based on thesequality assessments. A new QC criterion utilizing height errors within the GEOS ADAS is introducedto exclude data with slow wind speed biases and large errors. GEOS forecast accuracy is slightlyimproved after assimilating GK2A AMVs along with other conventional, radiance, and satellitewinds which include AMVs made by the Himawari-8 satellite in nearly the same observational areaof GK2A. Additionally, the present work shows that GEOS forecasts can be significantly improved,especially in the tropics and southern hemisphere after assimilating GK2A data in the absence ofHimawari-8 AMVs. This study demonstrates that GK2A AMV data is a valuable data source toenhance the robustness of GEOS ADAS.

Atmospheric Motion Vectors↗
NASA NTRS2022

pyCRTM: A Python Interface for the Community Radiative Transfer Model

The Community Radiative Transfer Model (CRTM) is a powerful and versatile scalar radiative transfer model for satellite data assimilation and remote sensing applications. It is implemented as an object-oriented Fortran library, enabling flexible code development and optimal runtime performance on clusters. The downsides of the Fortran interface are a steep learning curve for students and the reduced productivity of users that is typical for static compiled languages, in contrast to dynamic interpreted languages like Python. pyCRTM is a new software framework that directly interfaces the CRTM Fortran data structures and procedures in Python, leveraging both the simplicity and ease of use of Python syntax as well as the flexibility arising from the vast contemporary Python ecosystem. The goal of pyCRTM is to lower the barrier of entry for university students to learn and use the CRTM and to boost the productivity of researchers seeking to create new methods in radiative transfer and data assimilation, or seeking to apply the CRTM to study atmospheric phenomena without having to go through the pre-existing complexity of the CRTM Fortran interface.

Python↗
NASA NTRSDate not supplied

Assimilation of CrIS Shortwave Infrared Channels into the GEOS Atmospheric Data Assimilation System

In recent years, there has been a renewed interest in using the 4.3 μmshortwave infrared (SWIR) band for temperature sounding. This is in part brought on by proposed cubesat missions sensing the 4.3 μmband such as MiSTIC and CIRAS. Jones et al. has shown that shortwave infrared channels on CrIScan be used effectively in NOAA's Global Forecast and Data Assimilation System (GDAS). In this work a similar study is presented using the Goddard Earth Observing System - Atmospheric Data Assimilation System (GEOS-ADAS). Results from Observing System Experiments (OSEs) utilizing SWIR CrISare presented using standard community accepted forecast metrics including Forecast Sensitivity to Observation Impact (FSOI), and an assessment of vertical sensitivity using Jacobians from the Community Radiative Transfer Model (CRTM). The implications and utility within the GEOS-ADAS for future NASA GMAO products are discussed.

Bryan M Karpowicz↗
NASA NTRSDate not supplied

Assessment of Retrieved GMI Emissivity Over Land, Snow and Sea Ice in the GEOS System

Measurements from microwave sounders and imagers provide a valuable source of information including atmospheric temperature and water vapor in Numerical Weather Prediction (NWP) systems that assimilate these observations directly over water surfaces (oceans and other large water bodies). In a recent decadal survey, targeted observables in the Planetary Boundary Layer (PBL) were cited as a key need for future observations (NASEM, 2018). Microwave observations which sense in the PBL are currently available, however, utilizing surface-sensitive microwave observations for atmospheric data assimilation remains a challenge over land, snow and sea ice. This is in part due to the inability of surface emissivity models used by NWP data assimilation systems to simulate observations with sufficient accuracy. The GEOS-ADAS (Todling and el Akkraoui, 2018) which utilizes the Community Radiative Transfer Model (CRTM) (Han, 2006; Chen 2009) is no exception. The ECMWF system has retrieved instantaneous surface emissivity from surface-sensitive channels for SSMI/S and MHS radiance observations, and apply these estimates to the closest channels higher in frequency (Baordo and Geer 2016) in the calculation of simulated radiances. This approach currently is also being tested in the GEOS-ADAS for AMSU-A and ATMS radiances (Zhu et al. 2021). No or minimal emissivity spectral variability has been assumed in the above-mentioned studies. Recently, work by Munchak et al., 2020 (hereby referred to as M2020) provided a new database for emissivity over land, snow and sea ice retrieved from the NASA Global Precipitation Mission (GPM). Compared with Tool to Estimate Land Surface Emissivities at Microwave (TELSEM2; Wang et al., 2017), M2020 provides emissivities for more frequencies(i.e., 10.7 GHz V/H). Moreover, this database is unique in that it utilizes both active and passive data to retrieve surface emissivity and normalized radar cross section. While the emissivity values may be useful for other sensors, they are most applicable to the GPM Microwave Imager (GMI). In this work the GEOS-ADAS is modified to utilize emissivity values from Munchak et al, 2020 in place of values used by CRTM. Presently, only GMI radiances over ocean are used in the operational GEOS-ADAS. This study will focus on the GMI radiances over land, snow, and ice, as a first attempt to evaluate GMI radiances over these non-water surface types. Two cases are then presented, one with one week of observation minus background departures using the modified GEOS-ADAS, and one utilizing the original GEOS-ADAS. It should be noted that the surface emissivity models in CRTM are not state of the art and are scheduled to be replaced by the Community Surface Emissivity Module (CSEM; Chen and Weng, 2016). Simulations using default CRTM emissivity values are used merely as reference comparing against M2020, and is not a thorough comparison against other more state of the art modules such as CSEM.

GMI↗
NASA NTRSDate not supplied

Impacts of Assimilating Infrared Sounders from Geostationary Orbit

A set of observing system simulation experiments (OSSEs) was used to assess the impact of assimilating hyperspectral infrared (IR) radiances from geostationary platforms. This was done in preparation for the proposed National Oceanic and Atmospheric Administration (NOAA) Geostationary eXtended Observations (GeoXO) Sounder (GXS), expected to launch in the 2030s, using the National Aeronautics and Space Administration (NASA) Global Modeling and Assimilation Office (GMAO) OSSE framework. From a numerical weather prediction (NWP) perspective, a global “ring” of geostationary IR sounders was found to improve both the analysis and forecasts and provide beneficial impacts as measured by the forecast sensitivity observation impact (FSOI) metric.

Geostationary orbit↗
NASA NTRSDate not supplied

Numerical Weather Prediction Impact of GEO and LEO IR Sounders in an OSSE Framework

Low Earth Orbit (LEO) hyperspectral radiances from instruments such as CrIS, AIRS, and IASI are essential to NWP (among many other applications!), providing high vertical resolution T and humidity information. The United States is considering launching a hyperspectral sounder into a Geostationary Orbit (GEO) in the 2030s, creating a global ring with international partners. The combination of sounders in LEO and GEO provide both global coverage and high temporal resolution regional observations with the capability to dwell on high impact weather events.

Erica McGrath-Spangler↗
NASA NTRSDate not supplied

Complementarities of GEO and LEO IR Sounders for Numerical Weather Prediction in an OSSE Framework

Preparations are underway for the United States’ weather satellite program and the expected advancements in the coming decade. Among these changes is the launch of the proposed NOAA/NASA Geostationary eXtended Observations (GeoXO) Sounder (GXS), coordinated with international counterparts, to form a global ring of hyperspectral infrared (IR) sounders. Coincident with the progress of IR sounders from GEO platforms, the future of low Earth orbit (LEO) sounders is being re-envisioned after decades of beneficial impact in numerical weather prediction (NWP) systems. To address questions about the utility of the two platform types in concert, the Global Modeling and Assimilation Office (GMAO) Observing System Simulation Experiment (OSSE) framework was used to examine the roles of GEO and LEO sounders and their impact on forecast error reduction. Overall, inclusion of both types of platforms in the satellite program produces the largest forecast error reduction with LEO sounders having a strong impact on global skill and GEO sounders providing the most benefit on the scale of targeted regions.

Erica McGrath-Spangler↗