Cooperative Institute for Research in the Atmosphere

About: Cooperative Institute for Research in the Atmosphere is a based out in . It is known for research contribution in the topics: Snow & Data assimilation. The organization has 332 authors who have published 997 publications receiving 38835 citations. The organization is also known as: CIRA.

Assimilating AMSU-A Radiances in the TC Core Area with NOAA Operational HWRF (2011) and a Hybrid Data Assimilation System: Danielle (2010)

Abstract: A regional hybrid variational–ensemble data assimilation system (HVEDAS), the maximum likelihood ensemble filter (MLEF), is applied to the 2011 version of the NOAA operational Hurricane Weather Research and Forecasting (HWRF) model to evaluate the impact of direct assimilation of cloud-affected Advanced Microwave Sounding Unit-A (AMSU-A) radiances in tropical cyclone (TC) core areas The forward components of both the gridpoint statistical interpolation (GSI) analysis system and the Community Radiative Transfer Model (CRTM) are utilized to process and simulate satellite radiances The central strategies to allow the use of cloud-affected radiances are (i) to augment the control variables to include clouds and (ii) to add the model cloud representations in the observation forward models to simulate the microwave radiances The cloudy AMSU-A radiance assimilation in Hurricane Danielle's (2010) core area has produced encouraging results with respect to the operational cloud-cleared radiance preproces

An Evaluation of Analog-Based Postprocessing Methods across Several Variables and Forecast Models

Abstract: Recently, two analog-based postprocessing methods were demonstrated to reduce the systematic and random errors from Weather Research and Forecasting (WRF) Model predictions of 10-m wind speed over the central United States. To test robustness and generality, and to gain a deeper understanding of postprocessing forecasts with analogs, this paper expands upon that work by applying both analog methods to surface stations evenly distributed across the conterminous United States over a 1-yr period. The Global Forecast System (GFS), North American Mesoscale Forecast System (NAM), and Rapid Update Cycle (RUC) forecasts for screen-height wind, temperature, and humidity are postprocessed with the two analog-based methods and with two time series–based methods—a running mean bias correction and an algorithm inspired by the Kalman filter. Forecasts are evaluated according to a range of metrics, including random and systematic error components; correlation; and by conditioning the error distributions on lead ...

Can a regional-scale reduction of atmospheric CO 2 during the COVID-19 pandemic be detected from space? A case study for East China using satellite XCO 2 retrievals

Abstract: . The COVID-19 pandemic resulted in reduced anthropogenic carbon dioxide (CO 2 ) emissions during 2020 in large parts of the world. To investigate whether a regional-scale reduction of anthropogenic CO 2 emissions during the COVID-19 pandemic can be detected using space-based observations of atmospheric CO 2 , we have analysed a small ensemble of OCO-2 and GOSAT satellite retrievals of column-averaged dry-air mole fractions of CO 2 , i.e. XCO 2 . We focus on East China and use a simple data-driven analysis method. We present estimates of the relative change of East China monthly emissions in 2020 relative to previous periods, limiting the analysis to October-to-May periods to minimize the impact of biogenic CO 2 fluxes. The ensemble mean indicates an emission reduction by approximately 10 % ± 10 % in March and April 2020. However, our results show considerable month-to-month variability and significant differences across the ensemble of satellite data products analysed. For example, OCO-2 suggests a much smaller reduction ( ∼ 1 %–2 % ± 2 %). This indicates that it is challenging to reliably detect and to accurately quantify the emission reduction with current satellite data sets. There are several reasons for this, including the sparseness of the satellite data but also the weak signal; the expected regional XCO 2 reduction is only on the order of 0.1–0.2 ppm. Inferring COVID-19-related information on regional-scale CO 2 emissions using current satellite XCO 2 retrievals likely requires, if at all possible, a more sophisticated analysis method including detailed transport modelling and considering a priori information on anthropogenic and natural CO 2 surface fluxes.

A general method for modeling on irregular grids

Abstract: For simulation on a spherical surface, such as global numerical weather prediction, icosahedral grids are superior to their competitors in uniformity of grid mesh distance across the entire globe and lack of neighboring grid cells that share only a single vertex. Use of such a grid presents unique programming challenges related to iteration across grid cells and location of neighboring cells. Here we describe an icosahedral grid with a one-dimensional vector loop structure, table specified memory order, and an indirect addressing scheme that yields very compact code despite the complexities of this grid. This approach allows the same model code to be used for many grid structures. Indirect addressing also allows grid cells to be stored in any order, selectable at run time. This permits easy implementation of different memory layouts for cache blocking, distributed-memory parallelism, and static load balancing. Since indirect addressing can adversely affect execution time we organize arrays to place a directly addressable index innermost. We also describe experiments designed to measure any performance penalties accrued from use of indirect addressing.