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.

Bounds on the Variance in the Pattern Matching Criteria

Abstract: The Backus-Gilbert (BG) method is used throughout the satellite community as a noise/pattern adjustment technique. To proceed with the study of the discrete BG (DBG) method, the fundamental properties of the noise/pattern tradeoff need to be studied. This paper investigates the pattern matching criteria the DBG method is based on and how noise minimization is incorporated into the solution. In particular, the bounded nature of the variance and the behavior of the tradeoff curve are shown explicitly. The penalty function is studied, and restrictions on its definition are given; problems with the common choice of J=1 are also presented. The case of a positive semidefinite error covariance matrix is analyzed and the uniqueness of the solution evaluated. This paper serves to provide a thorough theoretical discussion of the noise/pattern tradeoff properties and the practical issues associated with its implementation

Which visibility indicators best represent a population’s preference for a level of visual air quality?

Abstract: Several studies have been carried out over the past 20 or so years to assess the level of visual air quality that is judged to be acceptable in urban settings. Groups of individuals were shown slid...

Design of a temporal variational data assimilation method suitable for deep soil moisture retrievals using passive Microwave radiometer data

Abstract: Our goal is to identify paths to the soil moisture performance objective (soil moisture at depths between 0–80 cm) for US Army and civilian use, and to identify and mitigate algorithm impediments to its potential performance. This work will also enable the Army to more accurately determine the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Soil Moisture Environmental Data Record (EDR) impacts upon DoD-related trafficability, off-road mobility, counter-mine operations, and hydrological stream flow estimation. Interactions and community involvement with a variety of agencies that will use the NPOESS surface and deep soil moisture products are also underway. A temporal variational data assimilation methodology is used to derive deep soil moisture profile sensitivities and tendencies for use with future NPOESS Microwave Imager Sounder (MIS) data. We have successfully completed our individual system component tests. Our current focus is full system integration within targeted DoD operational architectures. The components are being integrated into the development version of the Air Force Weather Agency (AFWA) – Land Information System (LIS). Design details of the various system components will be discussed. The land surface model and its respective adjoint sensitivities are used in a 4D variational (4DVAR) solver. We have adopted the Fletcher non-Gaussian 4DVAR framework, as soil moisture variables have skewed data distributions, and are therefore non-Gaussian. The 4DVAR solver component tests are based on lognormal probability distributions.

Satellite imagery and products of the 16???17 February 2020 Saharan Air Layer dust event over the eastern Atlantic: impacts of water vapor on dust detection and morphology

Abstract: . On 16–17 February 2020, dust within the Saharan Air Layer (SAL) from western Africa moved over the eastern Atlantic Ocean. Satellite imagery and products from the ABI on GOES-16, VIIRS on NOAA-20, and CALIOP on CALIPSO, along with retrieved values of layer and total precipitable water (TPW) from MIRS and NUCAPS, respectively, were used to identify dust within the SAL over the eastern Atlantic Ocean. Various satellite imagery and products were also used to characterize the distribution of water vapor within the SAL. There was a distinct pattern between dust detection and dust masking and values of precipitable water. Specifically, dust was detected when values of layer TPW were approximately 14 mm; in addition, dust was masked when values of layer TPW were approximately 28 mm. In other words, water vapor masked infrared dust detection if sufficient amounts of water vapor existed in a column. Results herein provide observational support to two recent numerical studies that concluded water vapor can mask infrared detection of airborne dust.

Hydrometeorological Assessment of Satellite and Model Precipitation Products over Taiwan

Abstract: Satellite and model precipitation such as the Global Precipitation Measurement (GPM) data are valuable in hydrometeorological applications. This study investigates the performance of various satellite and model precipitation products in Taiwan from 2015 to 2017, including data derived from the Integrated Multi-satellitE Retrievals for GPM Early and Final Runs (IMERG_E and IMERG_F), Global Satellite Mapping of Precipitation_near-real-time (GSMaP_NRT), and the Weather Research and Forecasting (WRF) model. We assess these products by comparing them against data collected from 304 surface stations and gauge-based gridded data. Our assessment emphasizes factors influential in precipitation estimation, such as season, temperature, elevation, and extreme event. Further, we assess the hydrological response to each precipitation product via continuous flow simulation in two selected watersheds. The results indicate that the performance of these precipitation products is subject to seasonal and regional variations. The satellite products (i.e., IMERG and GSMaP) perform better than the model (i.e., WRF) in the warm season and vice versa in the cold season, most apparently in northern Taiwan. For selected extreme events, WRF can simulate better rainfall amount and distribution. The seasonal and regional variations in precipitation estimation are also reflected in flow simulation: IMERG in general produces the most rational flow simulation, GSMaP tends to overestimate and be least useful for hydrological applications, while WRF simulates high flows that show accurate time to the peak flows and are better in the southern watershed.