With a goal of improving operational numerical weather prediction (NWP), the Developmental Testbed Center (DTC) has been working with operational centers, including, among others, the National Centers for Environmental Prediction (NCEP), National Oceanic and Atmospheric Administration (NOAA), National Aeronautics and Space Administration (NASA), and the U.S. Air Force, to support numerical models/systems and their research, perform objective testing and evaluation of NWP methods, and facilitate research-to-operations transitions. This article introduces the first attempt of the DTC in the data assimilation area to help achieve this goal. Since 2009, the DTC, NCEP’s Environmental Modeling Center (EMC), and other developers have made significant progress in transitioning the operational Gridpoint Statistical Interpolation (GSI) data assimilation system into a community-based code management framework. Currently, GSI is provided to the public with user support and is open for contributions from internal developers as well as the broader research community, following the same code transition procedures. This article introduces measures and steps taken during this community GSI effort followed by discussions of encountered challenges and issues. The purpose of this article is to promote contributions from the research community to operational data assimilation capabilities and, furthermore, to seek potential solutions to stimulate such a transition and, eventually, improve the NWP capabilities in the United States.
Data reduction tools are developed and evaluated using a data analysis framework. Simple (nonadaptive) and intelligent (adaptive) thinning algorithms are applied to both synthetic and real data and the thinned datasets are ingested into an analysis system. The approach is motivated by the desire to better represent highimpact weather features (e.g., fronts, jets, cyclones, etc.) that are often poorly resolved in coarse-resolution forecast models and to efficiently generate a set of initial conditions that best describes the current state of the atmosphere. As a precursor to real-data applications, the algorithms are applied to one-and two-dimensional synthetic datasets. Information gleaned from the synthetic experiments is used to create a thinning algorithm that combines the best aspects of the intelligent methods (i.e., their ability to detect regions of interest) while reducing the impacts of spatial irregularities in the data. Both simple and intelligent thinning algorithms are then applied to Atmospheric Infrared Sounder (AIRS) temperature and moisture profiles. For a given retention rate, background, and observation error, the optimal 1D analyses (i.e., lowest MSE) tend to have observations that are near regions of large curvature and gradients. Observation error leads to the selection of spurious data in homogeneous regions of the intelligent algorithms. In the 2D experiments, simple thinning tends to perform better within the homogeneous data regions. Analyses produced using AIRS data demonstrate that observations selected via a combination of the simple and intelligent approaches reduce clustering, provide a more even distribution along the satellite swath edges, and, in general, have lower error and comparable computational requirements compared to standard operational thinning methodologies.
Abstract. The Community Radiative Transfer Model (CRTM), a sensor-based radiative transfer model, has been used within the Gridpoint Statistical Interpolation (GSI) system for directly assimilating radiances from infrared and microwave sensors. We conducted numerical experiments to illustrate how including aerosol radiative effects in CRTM calculations changes the GSI analysis. Compared to the default aerosol-blind calculations, the aerosol influences reduced simulated brightness temperature (BT) in thermal window channels, particularly over dust-dominant regions. A case study is presented, which illustrates how failing to correct for aerosol transmittance effects leads to errors in meteorological analyses that assimilate radiances from satellite infrared sensors. In particular, the case study shows that assimilating aerosol-affected BTs significantly affects analyzed temperatures in the lower atmosphere across several regions of the globe. Consequently, a fully cycled aerosol-aware experiment improves 1–5 d forecasts of wind, temperature, and geopotential height in the tropical troposphere and Northern Hemisphere stratosphere. Whilst both GSI and CRTM are well documented with online user guides, tutorials, and code repositories, this article is intended to provide a joined-up documentation for aerosol absorption and scattering calculations in the CRTM and GSI. It also provides guidance for prospective users of the CRTM aerosol option and GSI aerosol-aware radiance assimilation. Scientific aspects of aerosol-affected BT in atmospheric data assimilation are briefly discussed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.