Ross N. Hoffman

An approach is outlined to quantitatively assess the socioeconomic benefits and cost effectiveness of Earth-observing systems. In a proof-of-concept demonstration, the benefits of different constellations of Earth-observing satellites are estimated using the methodology of the Advanced Systems Performance Evaluation Tool for NOAA (ASPEN). In ASPEN, the benefit is the degree to which an observing system or constellation of observing systems satisfies the application requirements weighted by the application priorities of those requirements. Here, a power-law transfer function converts the nowcasting application benefits into estimates of lives saved during weather-related catastrophic events (CAT events). With very substantial error bars, the current operational observing system is estimated to save more than 1200 lives annually that would otherwise be lost in U.S. CAT events. The variation of CAT deaths due to differences in potential versions of the constellation of Earth-observing satellites is described and compared to the estimated costs of the observing systems. Significance Statement: Many decisions are required 1) to plan the next constellation of Earth-observing satellites, 2) to design the sensors included in that system, and 3) to operationalize that system. In principle, these decisions should be guided by how the result affects society and the economy. However, to date, the best support for such decisions has been assessment tools that estimate the impact of observing systems on NWP forecasts. These tools can be cumbersome and expensive. In some studies—under the auspices of EUMETSAT, the Coordination Group for Meteorological Satellites (CGMS), and NOAA—the impact of improved weather information has been extended to socioeconomic impacts. The Advanced Systems Performance Evaluation Tool for NOAA (ASPEN) was developed to be a science-based and efficient tool for comparative assessment of observation systems. ASPEN also estimates the impacts of observing systems on applications including NWP and nowcasting. The purpose of this study is to translate ASPEN results into impacts on society and the economy. The proof-of-concept example presented is for deaths associated with weather-related catastrophic events (CAT events). For this demonstration, we envision a future in which CAT deaths are substantially reduced due to greatly improved nowcasting. This future scenario and the current state provide two points to define a transfer function (i.e., interpolation/extrapolation) from the ASPEN benefit to the number of CAT deaths. The quadratic extrapolation to the scenario of no weather satellite data gives an estimate that the current system saves more than 1200 lives annually from weather-related CAT events in the United States. In terms of the “value of a statistical life,” this corresponds to a savings of over 12 billion dollars annually, which is greater than the estimated annualized cost of the U.S. contribution to operational meteorological satellites.

The Advanced Systems Performance Evaluation Tool for NOAA (ASPEN) is applied to the problem of optimizing the design of a constellation of sensors by calculating the scientific benefit and cost effectiveness of all possible combinations of sensors within a specified design ensemble (i.e., a catalog of sensors). In this proof-of-concept study, sensors are derived from the NOAA Satellite Observing System Architecture (NSOSA) study, the Geostationary Extended Observations (GeoXO) satellite system program, and the COSMIC-2 mission, and the targeted applications are restricted to two examples, Global NWP and a suite of six nowcasting applications. The results are subject to several caveats, including the use of the current version of the ASPEN inputs and simplified methods of estimating costs. Achieving optimization adopts the approach of visualizing the results as cost–benefit “efficient frontier” scatterplots and identifying the most efficient constellations—the constellations that maximize the benefit for a given cost. The optimal constellation depends strongly on the budget, the sensor costs, the applications considered and their observational requirements and priorities, and the design ensemble of possible constellations. For illustration, a simple decision rule is applied to select the optimal constellation for a given budget. In practice, such guidance must be carefully considered in the context of neighboring constellations, i.e., constellations that are close to the optimal constellation in the efficient frontier scatterplot. In this study, for nominal ($2B annualized) costs, the optimal constellation includes the MW LEO Sounder, GNSS RO System, and VIS IR LEO Imager for Global NWP and the VIS IR GEO Imager, MW LEO Sounder, and IR GEO Sounder for nowcasting. Significance Statement: The Advanced Systems Performance Evaluation Tool for NOAA (ASPEN) is designed to be a science-based and efficient tool for comparative assessment of observing systems. ASPEN comparisons of observing system performances to application requirements are “observing system neutral” because the ASPEN interface characterizes the Earth system in terms of geophysical observables and their attributes. The ASPEN benefit combines the degree to which the performances of the observing systems fulfill the application requirements, weighted by the application priorities. Plotting the ASPEN benefit versus the cost of each potential constellation and then identifying those close to satisfying the budget requirements with the greatest benefits should be a useful tool to help optimize the next-generation space-based Earth-observing system.

Atmospheric motion vector (AMV) winds have positive impacts in operational numerical weather prediction (NWP) systems. These impacts might be improved with better treatment of the following error characteristics of AMVs. First, AMVs may have wind errors due to height assignment errors. Second, AMVs may have additional wind‐speed biases in addition to those due to height assignment errors. Third, AMVs are representative of motion in a possibly thick atmospheric layer, not a single atmospheric level. Previous work proposed a variational feature track correction (FTC) method in which an observation operator is implemented that averages the NWP background winds optimally in the vertical. Here, a prototype feature track correction observation operator (FTC‐OO) is implemented in the NOAA/NCEP data assimilation (DA) system. The parameters describing the vertical averaging are determined offline based on previous DA cycles. The FTC‐OO reduces the observation minus background standard deviation by about 4%. Global observing‐system experiments (OSEs) are performed comparing the FTC‐OO with the operational observation operator. The forecast verification sample is 41 10‐day forecasts. The OSEs show that the FTC‐OO improves forecast skill, primarily for tropical geopotential height. Additional OSEs are performed that include Aeolus wind observations. The hypothesis that the Aeolus winds would enhance the impact of the FTC‐OO was not borne out in these experiments—the Aeolus observations alone have a significant positive impact, but the impact of the FTC method in the presence of the Aeolus observations is neither enhanced nor degraded compared with the impact of the FTC method alone.

Accurate atmospheric 3D wind observations are one of the top priorities for the global scientific community. To address this requirement, and to support researchers’ needs to acquire and analyze wind data from multiple sources, the System for Analysis of Wind Collocations (SAWC) was jointly developed by NOAA/NESDIS/STAR, UMD/ESSIC/CISESS, and UW-Madison/CIMSS. SAWC encompasses the following: a multi-year archive of global 3D winds observed by Aeolus, sondes, aircraft, stratospheric superpressure balloons, and satellite-derived atmospheric motion vectors, archived and uniformly formatted in netCDF for public consumption; identified pairings between select datasets collocated in space and time; and a downloadable software application developed for users to interactively collocate and statistically compare wind observations based on their research needs. The utility of SAWC is demonstrated by conducting a one-year (September 2019–August 2020) evaluation of Aeolus level-2B (L2B) winds (Baseline 11 L2B processor version). Observations from four archived conventional wind datasets are collocated with Aeolus. The recommended quality controls are applied. Wind comparisons are assessed using the SAWC collocation application. Comparison statistics are stratified by season, geographic region, and Aeolus observing mode. The results highlight the value of SAWC’s capabilities, from product validation through intercomparison studies to the evaluation of data usage in applications and advances in the global Earth observing architecture.

The European Space Agency Aeolus mission launched a first-of-its-kind spaceborne Doppler wind lidar in August 2018. To optimize the assimilation of the Aeolus Level-2B (B10) horizontal line-of-sight (HLOS) winds, significant systematic differences between the observations and numerical weather prediction (NWP) background winds should be removed. Total least squares (TLS) regression is used to estimate speed-dependent systematic differences between the Aeolus HLOS winds and the National Oceanic and Atmospheric Administration (NOAA) Finite-Volume Cubed-Sphere Global Forecast System (FV3GFS) 6 h forecast winds. Unlike ordinary least squares regression, TLS regression optimally accounts for random errors in both predictors and predictands. Large, well-defined, speed-dependent systematic differences are found in the lower stratosphere and troposphere in the tropics and Southern Hemisphere. Correction of these systematic differences improves the forecast impact of Aeolus data assimilated into the NOAA global NWP system.

The European Space Agency Aeolus mission launched the first‐of‐its‐kind space‐borne Doppler wind lidar in August 2018. The Aeolus Level‐2B (L2B) Horizontal Line‐of‐Sight (HLOS) wind observations are integrated into the NOAA Finite‐Volume Cubed‐Sphere Global Forecast System (FV3GFS). Components of the data assimilation system are optimized to increase the forecast impact from these Aeolus observations. Three observing‐system experiments (OSEs) are performed using the Aeolus L2B HLOS winds for the period of August 2–September 16, 2019: a baseline experiment assimilating all observations that are operationally assimilated in NOAA's FV3GFS but without Aeolus; an experiment adding the Aeolus L2B HLOS winds on top of the baseline configuration; and an experiment adding the Aeolus L2B HLOS winds on top of the baseline but also including a total least‐squares (TLS) regression bias correction applied to the HLOS winds. The variances of the Aeolus HLOS wind random errors (i.e., observation errors) are estimated using the Hollingsworth–Lonnberg (HL) method. Results from both OSEs demonstrate positive impact of Aeolus L2B HLOS winds on the NOAA global forecast. The largest impact is seen in the tropical upper troposphere and lower stratosphere where the Day 1–3 wind vector forecast root‐mean‐square error (RMSE) is reduced by up to 4%. Additionally, the assimilation of Aeolus impacts the steering currents ambient to tropical cyclones, resulting in a 15% reduction in track forecast error in the Eastern Pacific basin Day 2–5 forecasts, and a 5% and 20% reduction in track forecast error in the Atlantic basin at Day 2 and Day 5, respectively. In most cases, the additional TLS bias correction increases the positive impact of Aeolus data assimilation in the NOAA global numerical weather prediction (NWP) system when compared to the assimilation of Aeolus without bias correction.

The need for highly accurate atmospheric wind observations is a high priority in the science community, particularly for numerical weather prediction (NWP). To address this need, this study leverages Aeolus wind lidar level-2B data provided by the European Space Agency (ESA) as a potential comparison standard to better characterize atmospheric motion vector (AMV) bias and uncertainty. AMV products from geostationary (GEO) and low Earth orbiting (LEO) satellites are compared with reprocessed Aeolus horizontal line-of-sight (HLOS) global winds observed in August–September 2019. Winds from two Aeolus observing modes are compared with AMVs, namely Rayleigh-clear (RAY; derived from the molecular scattering signal) and Mie-cloudy (MIE; derived from the particle scattering signal). Quality-controlled (QC'd) Aeolus winds are co-located with QC'd AMVs in space and time, and the AMVs are projected onto the Aeolus HLOS direction. Mean co-location differences (MCDs) and the standard deviation (SD) of those differences (SDCDs) are determined and analyzed. As shown in other comparison studies, the level of agreement between AMV and Aeolus wind velocities (HLOSVs) varies with the AMV type, geographic region, and height of the co-located winds, as well as with the Aeolus observing mode. In terms of global statistics, QC'd AMVs and QC'd Aeolus HLOSVs are highly correlated for both observing modes. Aeolus MIE winds are shown to have great potential value as a comparison standard to characterize AMVs, as MIE co-locations generally exhibit smaller biases and uncertainties compared to RAY co-locations. Aeolus RAY winds contribute a substantial fraction of the total SDCDs in the presence of clouds where co-location/representativeness errors are also large. Stratified comparisons with Aeolus HLOSVs are consistent with known AMV bias and uncertainty in the tropics, NH extratropics, the Arctic, and at mid- to upper-levels in clear and cloudy scenes. AMVs in the SH/Antarctic generally exhibit larger-than-expected MCDs and SDCDs, most probably due to larger AMV height assignment errors and co-location/representativeness errors in the presence of high wind speeds and strong vertical wind shear, particularly for RAY comparisons.

The European Space Agency Aeolus mission launched the first of its kind spaceborne Doppler wind lidar in August 2018. To optimize assimilation of the Aeolus Level-2B (L2B) Horizontal Line-of-Sight (HLOS) winds, systematic differences (referred as biases hereafter) between the observations and numerical weather prediction (NWP) background winds should be removed. Total least squares (TLS) regression is used to estimate speed-dependent biases between Aeolus HLOS winds (L2B10) and the National Oceanic and Atmospheric Administration (NOAA) Finite-Volume Cubed-Sphere Global Forecast System (FV3GFS) 6-h forecast winds. Unlike ordinary least squares regression, TLS regression optimally accounts for random errors in both predictors and predictands. Large well-defined, speed-dependent biases are found particularly in the lower stratosphere and troposphere of the tropics and Southern Hemisphere. These large biases should be corrected to increase the forecast impact of Aeolus data assimilated into global NWP systems.