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NASA NTRS2000

Sensing Water Vapon via Spacecraft Radio Occultation Observations

The radio occultation technique has been used to characterize planetary atmospheres since the 1960's spanning atmospheric pressures from 16 microbars to several bars. In 1988, the use of GPS signals to make occultation observations of Earth's atmosphere was realized by Tom Yunck and Gunnar Lindal at JPL. In the GPS to low-Earth-orbiter limb- viewing occultation geometry, Fresnel diffraction yield a unique combination of high vertical resolution of 100 m to 1 km at long wavelengths (approx. 20 cm) insensitive to particulate scattering which allows routine limb sounding from the lower mesosphere through the troposphere. A single orbiting GPS/GLONASS receiver can observe - 1000 to 1400 daily occultations providing as many daily, high vertical resolution soundings as the present global radiosonde network, but with far more evenly distributed, global coverage. The occultations yield profiles of refractivity as a function of height. In the cold, dry conditions of the upper troposphere and above (T less than 240 K), profiles of density, pressure (geopotential), and temperature can be derived. Given additional temperature information, water vapor can be derived in the midddle and lower troposphere with a unique combination of vertical resolution, global distribution and insensitivity to clouds and precipitation to an accuracy of approx. 0.2 g/kg. At low latitudes, moisture profiles will be accurate to 1-5% within the convective boundary layer and better than 20% below 6 to 7 km. Accuracies of climatological averages should be approx. 0. 1 g/kg limited by the biases in the temperature estimates. To use refractivity to constrain water vapor, knowledge of temperature is required. The simplest approach is to use the temperature field from an analysis such as the 6 hour ECMWF global analysis interpolated to the locations of each occultation. A better approach is to combine the temperature and moisture fields from such an analysis with the occultation refractivity in a weighting scheme based on the errors in each data field. A ID variational combinational approach has been developed at the UKMO. We win present results from both approaches from GPS/MET data taken in June and July 1995 and compare them with the ECMWF global 6 hour moisture analyses which are derived largely from TOVS and radiosonde data. Overall, the atmosphere below the 500 mb level appears somewhat drier in general than the ECNIWF humidity field. A 2-D (latitude vs. height) climatological snapshot derived from a 2-week span of GPS/MET data will be compared to the humidity climatology of Peixoto and Oort derived from radiosonde data from 1963-1973. Differences between the GPS results and Peixoto and Oort may be the signature of a climate trend over the past 30 years.

Kursinski, E. Robert↗

DOE OSTI2022

Dynamics of ENSO Phase–Locking and Its Biases in Climate Models

Here, the contributions of different oceanic feedbacks to the El Niño–Southern Oscillation (ENSO) phase-locking are examined by deriving ENSO dynamics based on the recharge-discharge framework. In observations, the significant winter preference of the ENSO peak is determined by a strong seasonal modulation of SST growth rate, which is controlled by the zonal advective and thermodynamic feedbacks. However, the majority of climate models fail to simulate ENSO phase-locking because the contribution of zonal advective feedback to the seasonal modulation of the SST growth rate is much smaller compared to observations. The weak annual cycle of the SST-current coupling coefficient and small annual mean of the negative climatological zonal SST gradient are two factors contributing to the weak-biased seasonality of zonal advective feedback. Further analysis shows that the Niño3.4 SSTA has better phase-locking performance than Niño3 SSTA in the climate models due to the better simulation of zonal advection feedback in the Niño3.4 region.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Subseasonal Representation and Predictability of North American Weather Regimes Using Cluster Analysis

Abstract This study focuses on assessing the representation and predictability of North American weather regimes, which are persistent large-scale atmospheric patterns, in a set of initialized subseasonal reforecasts created using the Community Earth System Model, version 2 (CESM2). The k -means clustering was used to extract four key North American (10°–70°N, 150°–40°W) weather regimes within ERA5 reanalysis, which were used to interpret CESM2 subseasonal forecast performance. Results show that CESM2 can recreate the climatology of the four main North American weather regimes with skill but exhibits biases during later lead times with overoccurrence of the West Coast high regime and underoccurrence of the Greenland high and Alaskan ridge regimes. Overall, the West Coast high and Pacific trough regimes exhibited higher predictability within CESM2, partly related to El Niño. Despite biases, several reforecasts were skillful and exhibited high predictability during later lead times, which could be partly attributed to skillful representation of the atmosphere from the tropics to extratropics upstream of North America. The high predictability at the subseasonal time scale of these case-study examples was manifested as an “ensemble realignment,” in which most ensemble members agreed on a prediction despite ensemble trajectory dispersion during earlier lead times. Weather regimes were also shown to project distinct temperature and precipitation anomalies across North America that largely agree with observational products. This study further demonstrates that unsupervised learning methods can be used to uncover sources and limits of subseasonal predictability, along with systematic biases present in numerical prediction systems. Significance Statement North American weather regimes are large-scale atmospheric patterns that can persist for several days. Their skillful subseasonal (2 weeks or greater) prediction can provide valuable lead time to prepare for temperature and precipitation anomalies that can stress energy and water resources. The purpose of this study was to assess the climatological representation and subseasonal predictability of four key North American weather regimes using a research subseasonal prediction system and clustering analysis. We found that the Pacific trough and West Coast high regimes exhibited higher predictability than other regimes and that skillful representation of conditions across the tropics and extratropics can increase predictability during later lead times. Future work will quantify causal pathways associated with high predictability.

58 GEOSCIENCES↗

NASA NTRS2018

A Quick Summary of IMERG Versions and Features

This talk will summarize the shifts in IMERG (Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement)) from Version 03 to 04 in early Spring 2016, and to Version 05 in late Summer 2017. For example, Version 04 replaced approximate pre-launch calibrations with GPM Core Observatory-based calibrations, while Version 05 introduced improved estimates for the primary GPM instrument products (DPR, GMI, and Combined Instrument). In Version 04 the IR estimates were routinely calibrated to the passive microwave estimates. As analysis showed that the Combined Instrument estimates (the IMERG calibration standard) tend to be biased high over land and low over ocean at higher latitudes, in Version 04 we climatologically calibrated IMERG to the Global Precipitation Climatology Project (GPCP) monthly Satellite-Gauge product, except in low- and mid-latitude ocean regions. This calibration leaves the relative time series intact, and only adjusts the mean of the entire series. In Version 05 the primary GPM instrument products have reduced biases, but calibration to GPCP continues to be necessary to achieve the most realistic estimates. Finally, retrospective processing back into the TRMM (Tropical Rainfall Measuring Mission) era is expected in early 2018, after which the legacy TMPA (TRMM Multi-satellite Precipitation Analysis) dataset will be retired.

Huffman, George J.↗

DOE OSTI2024

Evaluation of WSR-88D Level III and MRMS Rainfall Estimates Against Rain Gauge Observations at the Savannah River Site

Rainfall data for the Savannah River Site (SRS) has been historically measured by rain gauges. These instruments serve as ground truth for most climatological and weather applications; however, gauge measurements are prone to errors or biases under certain weather conditions. Rainfall estimates from radar reflectivity values have been developed and improved over the years and serve as an alternative or supplement for gauge measurements. This study compares measurements from tipping bucket rain gauges with rainfall estimates from the NOAA NWS WSR-88D Level III hourly rainfall product and the Multi-Radar Multi-Sensor (MRMS) gauge-corrected precipitation estimate. Results show good agreement between radar derived amounts and ground measurements, with MRMS values showing correlation coefficients of 0.60-0.95 and RMSE values of less than 1.0 cm (0.4 in). The WSR-88D Level III estimates result in correlation coefficients of 0.46-0.86 and RMSE values of less than 1.3 cm (0.5 in). Few outliers are observed for each data pair and are evaluated against precipitation classification products (WSR-88D Hybrid Hydrometeor Classification product and MRMS Precipitation Flag product). The rain gauges used in this study are not part of the Hydrometeorological Automated Data System (HADS) network used to correct the MRMS estimates and therefore, results of this work provide an independent validation of the MRMS gauge correction scheme.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2017

Data Assimilation to Extract Soil Moisture Information From SMAP Observations

Statistical techniques permit the retrieval of soil moisture estimates in a model climatology while retaining the spatial and temporal signatures of the satellite observations. As a consequence, they can be used to reduce the need for localized bias correction techniques typically implemented in data assimilation (DA) systems that tend to remove some of the independent information provided by satellite observations. Here, we use a statistical neural network (NN) algorithm to retrieve SMAP (Soil Moisture Active Passive) surface soil moisture estimates in the climatology of the NASA Catchment land surface model. Assimilating these estimates without additional bias correction is found to significantly reduce the model error and increase the temporal correlation against SMAP CalVal in situ observations over the contiguous United States. A comparison with assimilation experiments using traditional bias correction techniques shows that the NN approach better retains the independent information provided by the SMAP observations and thus leads to larger model skill improvements during the assimilation. A comparison with the SMAP Level 4 product shows that the NN approach is able to provide comparable skill improvements and thus represents a viable assimilation approach.

D↗

DOE OSTI2021

A Data Set for Intercomparing the Transient Behavior of Dynamical Model-Based Subseasonal to Decadal Climate Predictions

Climate predictions using coupled models in different time scales, from intraseasonal to decadal, are usually affected by initial shocks, drifts, and biases, which reduce the prediction skill. These arise from inconsistencies between different components of the coupled models and from the tendency of the model state to evolve from the prescribed initial conditions toward its own climatology over the course of the prediction. Aiming to provide tools and further insight into the mechanisms responsible for initial shocks, drifts, and biases, this paper presents a novel data set developed within the Long Range Forecast Transient Intercomparison Project, LRFTIP. This data set has been constructed by averaging hindcasts over available prediction years and ensemble members to form a hindcast climatology, that is a function of spatial variables and lead time, and thus results in a useful tool for characterizing and assessing the evolution of errors as well as the physical mechanisms responsible for them. A discussion on such errors at the different time scales is provided along with plausible ways forward in the field of climate predictions

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2014

Bias Correction for Assimilation of Retrieved AIRS Profiles of Temperature and Humidity

Atmospheric Infrared Sounder (AIRS) is a hyperspectral radiometer aboard NASA's Aqua satellite designed to measure atmospheric profiles of temperature and humidity. AIRS retrievals are assimilated into the Weather Research and Forecasting (WRF) model over the North Pacific for some cases involving "atmospheric rivers". These events bring a large flux of water vapor to the west coast of North America and often lead to extreme precipitation in the coastal mountain ranges. An advantage of assimilating retrievals rather than radiances is that information in partly cloudy fields of view can be used. Two different Level 2 AIRS retrieval products are compared: the Version 6 AIRS Science Team standard retrievals and a neural net retrieval from MIT. Before assimilation, a bias correction is applied to adjust each layer of retrieved temperature and humidity so the layer mean values agree with a short-term model climatology. WRF runs assimilating each of the products are compared against each other and against a control run with no assimilation. This paper will describe the bias correction technique and results from forecasts evaluated by validation against a Total Precipitable Water (TPW) product from CIRA and against Global Forecast System (GFS) analyses.

Blankenship, Clay↗

DOE OSTI2021

Future projections in tropical cyclone activity over multiple CORDEX domains from RegCM4 CORDEX-CORE simulations

The characteristics of tropical cyclone (TC) activity over five TC basins lying within four Coordinated Regional Downscaling Experiment (CORDEX) domains are examined for present and future climate conditions using a new ensemble of simulations completed as part of the CORDEX-CORE initiative with the regional climate model RegCM4. The simulations are conducted at a 25 km horizontal grid spacing and are driven by three CMIP5 general circulation models (GCMs) under two Representative Concentration Pathways (RCP2.6 and RCP8.5). The RegCM4 captures most features of the observed TC climatology, except for the TC intensity, which is thus statistically adjusted using a bias correction procedure to account for the effect of the coarse model resolution. The RegCM4 exhibits an improved simulation of several TC statistics compared to the driving GCMs, over most basins analyzed. In future climate conditions we find significant increases in TC frequency over the North Indian Ocean, the Northwest Pacific and Eastern Pacific regions, which are consistent with an increase in mid-tropospheric relative humidity. The North Atlantic and Australasia regions show a decrease in TC frequency, mostly associated with an increase in wind shear. We also find a consistent increase in future storm rainfall rates associated with TCs and in the frequency of the most intense TCs over most domains. Finally, our study shows robust responses often, but not always, in line with previous studies, still implying the presence of significant uncertainties in the projection of TC characteristics, which need to be addressed using large ensembles of simulations with high-resolution models.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2017

Estimating Surface Soil Moisture from SMAP Observations Using a Neural Network Technique

A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to March 2017 with a 2-3 day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system, and Moderate Resolution Imaging Spectroradiometer-based vegetation water content. The NN was trained on GEOS-5 soil moisture target data, making the NN estimates consistent with the GEOS-5 climatology, such that they may ultimately be assimilated into this model without further bias correction. Evaluated against in situ soil moisture measurements, the average unbiased root mean square error (ubRMSE), correlation and anomaly correlation of the NN retrievals were 0.037 m(exp. 3)m(exp. -3), 0.70 and 0.66, respectively, against SMAP core validation site measurements and 0.026 m(exp. 3)m(exp. -3), 0.58 and 0.48, respectively, against International Soil Moisture Network (ISMN) measurements. At the core validation sites, the NN retrievals have a significantly higher skill than the GEOS-5 model estimates and a slightly lower correlation skill than the SMAP Level-2 Passive (L2P) product. The feasibility of the NN method was reflected by a lower ubRMSE compared to the L2P retrievals as well as a higher skill when ancillary parameters in physically-based retrievals were uncertain. Against ISMN measurements, the skill of the two retrieval products was more comparable. A triple collocation analysis against Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced Scatterometer (ASCAT) soil moisture retrievals showed that the NN and L2P retrieval errors have a similar spatial distribution, but the NN retrieval errors are generally lower in densely vegetated regions and transition zones.

Soil Moisture Remote Sensing↗

NASA NTRS2021

Residual Temperature Bias Effects in Stratospheric Species Distributions from LIMS

The Nimbus 7 Limb Infrared Monitor of theStratosphere (LIMS) instrument operated from 25 Octo-ber 1978 through 28 May 1979. Its version 6 (V6) profileswere processed and archived in 2002. We present severaldiagnostic examples of the quality of the V6 stratospheric species distributions based on their level 3 zonal Fourier co-efficient products. In particular, we show that there are smalldifferences in the ascending (A) minus descending (D) or-bital temperature–pressure orT(p) profiles (theirA−Dval-ues) that affect (A−D) species values. SystematicA−Dbi-ases inT(p) can arise from small radiance biases and/or fromviewing anomalies along orbits. There can also be (A−D)differences inT(p) due to not resolving and correcting forall of the atmospheric temperature gradient along LIMS tan-gent view-paths. An error inT(p) affects species retrievalsthrough (1) the Planck blackbody function in forward calcu-lations of limb radiance that are part of the iterative retrievalalgorithm of LIMS, and (2) the registration of the measuredLIMS species radiance profiles in pressure altitude, mainlyfor the lower stratosphere. There are clearA−Ddifferencesfor ozone, H2O, and HNO3but not for NO2. Percentage differences are larger in the lower stratosphere for ozone andH2O because those species are optically thick. We evaluateV6 ozone profile biases in the upper stratosphere with the aid of comparisons against a monthly climatology of UV–ozone soundings from rocketsondes. We also provide resultsof time series analyses of V6 ozone, H2O, and potential vor-ticity for the middle stratosphere to show that their average(A+D) V6 level 3 products provide a clear picture of the evolution of those tracers during Northern Hemisphere win-ter. We recommend that researchers use the average V6 level3 product for their science studies of stratospheric ozone andH2O, while keeping in mind that there are uncorrected non-local thermodynamic equilibrium effects in daytime ozone inthe lower mesosphere and in daytime H2O in the uppermoststratosphere. We also point out that the present-day Soundingof the Atmosphere using Broadband Emission Radiometry(SABER) experiment provides measurements and retrievalsof temperature and ozone that are nearly free of anomalousdiurnal variations and of effects from gradients at low and middle latitudes.

Ellis Remsberg↗

NASA NTRS2012

Inferring Land Surface Model Parameters for the Assimilation of Satellite-Based L-Band Brightness Temperature Observations into a Soil Moisture Analysis System

The Soil Moisture and Ocean Salinity (SMOS) satellite mission provides global measurements of L-band brightness temperatures at horizontal and vertical polarization and a variety of incidence angles that are sensitive to moisture and temperature conditions in the top few centimeters of the soil. These L-band observations can therefore be assimilated into a land surface model to obtain surface and root zone soil moisture estimates. As part of the observation operator, such an assimilation system requires a radiative transfer model (RTM) that converts geophysical fields (including soil moisture and soil temperature) into modeled L-band brightness temperatures. At the global scale, the RTM parameters and the climatological soil moisture conditions are still poorly known. Using look-up tables from the literature to estimate the RTM parameters usually results in modeled L-band brightness temperatures that are strongly biased against the SMOS observations, with biases varying regionally and seasonally. Such biases must be addressed within the land data assimilation system. In this presentation, the estimation of the RTM parameters is discussed for the NASA GEOS-5 land data assimilation system, which is based on the ensemble Kalman filter (EnKF) and the Catchment land surface model. In the GEOS-5 land data assimilation system, soil moisture and brightness temperature biases are addressed in three stages. First, the global soil properties and soil hydraulic parameters that are used in the Catchment model were revised to minimize the bias in the modeled soil moisture, as verified against available in situ soil moisture measurements. Second, key parameters of the "tau-omega" RTM were calibrated prior to data assimilation using an objective function that minimizes the climatological differences between the modeled L-band brightness temperatures and the corresponding SMOS observations. Calibrated parameters include soil roughness parameters, vegetation structure parameters, and the single scattering albedo. After this climatological calibration, the modeling system can provide L-band brightness temperatures with a global mean absolute bias of less than 10K against SMOS observations, across multiple incidence angles and for horizontal and vertical polarization. Third, seasonal and regional variations in the residual biases are addressed by estimating the vegetation optical depth through state augmentation during the assimilation of the L-band brightness temperatures. This strategy, tested here with SMOS data, is part of the baseline approach for the Level 4 Surface and Root Zone Soil Moisture data product from the planned Soil Moisture Active Passive (SMAP) satellite mission.

Reichle, Rolf H.↗

NASA NTRS1994

Use of multiple gauges and microwave attenuation of precipitation for satellite verification

In this paper both a microwave attenuation measurement along a horizontal line and multiple point gauge measurements are analyzed as possible ground-truth designs to validate satellite precipitation retrieval algorithms at the field of view spatial level (typically about 20 km). The design consists of comparing a sequence of pairs of contemporaneous measurements taken from the ground and from space. The authors examine theoretically the variance of expected differences between the two systems. The line average measurement leads to a smaller mean-square error compared to the case of a single point gauge, since some of the small-scale variability of the rain field is smoothed away by the line integration. The multiple point gauge measurements also give smaller mean-square error than that of a single point gauge. The centroid of the line and point gauge configurations are considered to be located randomly inside the field of view for different overpasses. A space-time spectral formalism is used with a noise-forced diffusive rain field to find the mean-square error. By considering instantaneous ground and satellite measurement pairs over about 50 visits when raining, we can reduce the expected error to approximately 10% of the standard deviation of climatological variability. This is considered to be a useful level of tolerance for identifying biases in the retrieval algorithms. It is found that the multiple point gauges (especially two gauges) are the economical ground-truth design compared to the microwave attenuation based on the mean-square error comparison. The major finding of this study is that a significant improvement over the point gauge is obtained by adding a single additional piece of information; adding more gauges or extending the line of attenuation is not an important improvement.

Ha, Eunho↗

NASA NTRS2014

Use of 2d-video Disdrometer to Derive Mean Density-size and Ze-SR Relations: Four Snow Cases from the Light Precipitation Validation Experiment

The application of the 2D-video disdrometer to measure fall speed and snow size distribution and to derive liquid equivalent snow rate, mean density-size and reflectivity-snow rate power law is described. Inversion of the methodology proposed by Böhm provides the pathway to use measured fall speed, area ratio and '3D' size measurement to estimate the mass of each particle. Four snow cases from the Light Precipitation Validation Experiment are analyzed with supporting data from other instruments such as Precipitation Occurrence Sensor System (POSS), Snow Video Imager (SVI), a network of seven snow gauges and three scanning C9 band radars. The radar-based snow accumulations using the 2DVD-derived Ze-SR relation are in good agreement with a network of seven snow gauges and outperform the accumulations derived from a climatological Ze-SR relation used by the Finnish Meteorological Institute (FMI). The normalized bias between radar-derived and gauge accumulation is reduced from 96% when using the fixed FMI relation to 28% when using the Ze-SR relations based on 2DVD data. The normalized standard error is also reduced significantly from 66% to 31%. For two of the days with widely different coefficients of the Ze-SR power law, the reflectivity structure showed significant differences in spatial variability. Liquid water path estimates from radiometric data also showed significant differences between the two cases. Examination of SVI particle images at the measurement site corroborated these differences in terms of unrimed versus rimed snow particles. The findings reported herein support the application of Böhm's methodology for deriving the mean density-size and Ze-SR power laws using data from 2D-video disdrometer.

radar↗

NASA NTRS2004

Tower Mesonetwork Climatology and Interactive Display Tool

Forecasters at the 45th Weather Squadron and Spaceflight Meteorology Group use data from the tower network over the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) to evaluate Launch Commit Criteria, and issue and verify forecasts for ground operations. Systematic biases in these parameters could adversely affect an analysis, forecast, or verification. Also, substantial geographical variations in temperature and wind speed can occur under specific wind directions. To address these concerns, the Applied Meteorology Unit (AMU) developed a climatology of temperatures and winds from the tower network, and identified the geographical variation and significant tower biases. The mesoclimate is largely driven by the complex land-water interfaces across KSC/CCAFS. Towers with close proximity to water typically had much warmer nocturnal temperatures and higher wind speeds throughout the year. The strongest nocturnal wind speeds occurred from October to March whereas the strongest mean daytime wind speeds occurred from February to May. These results of this project can be viewed by forecasters through an interactive graphical user interface developed by the AMU. The web-based interface includes graphical and map displays of mean, standard deviation, bias, and data availability for any combination of towers, variables, months, hours, and wind directions.

Case, Jonathan L.↗

NASA NTRS2021

Improved Representation of Atmospheric Dynamics in CMIP6 Models Removes Climate Sensitivity Dependence on Hadley Cell Climatological Extent

The persistent inter-model spread in the response of global mean surface temperature to increased CO. (known as the “Equilibrium Climate Sensitivity”, or “ECS”) is a crucial problem across model generations. This work examines the influence of the models’ present-day atmospheric circulation climatologies, and the accompanying climatological cloud radiative effects, in explaining that spread. We analyze the Coupled Model Intercomparison Project Phase 6 (CMIP6) models and _nd that they simulate a more poleward, and thus more realistic, edge of the Hadley Circulation (HC) in the SouthernHemisphere than the CMIP5 models, although the climatological shortwave cloud radiative effects are similar in the two generations of models. A few CMIP5 models with extreme equatorward biases in the HC edge exhibited high ECS due to strong Southern midlatitude shortwave cloud radiative warming in response to climate change, suggesting an ECS dependence on HC position. We find that such constraint no longer holds for the CMIP6 models, due to absence of models with extreme HC climatologies. In spite of this, however, the CMIP6 models show an increased spread in ECS, with more models in the high ECS range. Additionally, an improved representation of the climatological jet dynamics does not lead to a new emergent constraint in the CMIP6 models either.

Equilibrium Climate Sensitivity↗

NASA NTRS2010

Satellite Sampling and Retrieval Errors in Regional Monthly Rain Estimates from TMI AMSR-E, SSM/I, AMSU-B and the TRMM PR

Passive and active microwave rain sensors onboard earth-orbiting satellites estimate monthly rainfall from the instantaneous rain statistics collected during satellite overpasses. It is well known that climate-scale rain estimates from meteorological satellites incur sampling errors resulting from the process of discrete temporal sampling and statistical averaging. Sampling and retrieval errors ultimately become entangled in the estimation of the mean monthly rain rate. The sampling component of the error budget effectively introduces statistical noise into climate-scale rain estimates that obscure the error component associated with the instantaneous rain retrieval. Estimating the accuracy of the retrievals on monthly scales therefore necessitates a decomposition of the total error budget into sampling and retrieval error quantities. This paper presents results from a statistical evaluation of the sampling and retrieval errors for five different space-borne rain sensors on board nine orbiting satellites. Using an error decomposition methodology developed by one of the authors, sampling and retrieval errors were estimated at 0.25 resolution within 150 km of ground-based weather radars located at Kwajalein, Marshall Islands and Melbourne, Florida. Error and bias statistics were calculated according to the land, ocean and coast classifications of the surface terrain mask developed for the Goddard Profiling (GPROF) rain algorithm. Variations in the comparative error statistics are attributed to various factors related to differences in the swath geometry of each rain sensor, the orbital and instrument characteristics of the satellite and the regional climatology. The most significant result from this study found that each of the satellites incurred negative longterm oceanic retrieval biases of 10 to 30%.

Fisher, Brad↗

NASA NTRS2023

Interpretation of Probabilistic Surface Ozone Forecasts: A Case Study for Philadelphia

The use of probabilistic forecasting has been growing in a variety of disciplines because of its potential to emphasize the degree of uncertainty inherent in a prediction. Interpretation of probabilistic forecasts, however, is oftentimes difficult, deterring users who may benefit from such forecasts. To encourage broader use of probabilistic forecasts in the field of air quality, a process for interpreting forecasts from a statistical probabilistic air quality surface ozone model [the Regression in Self Organizing Map (REGiS)] is demonstrated. Four procedures to convert probabilistic to deterministic forecasts are explored for the Philadelphia, Pennsylvania, metropolitan area. These procedures calibrate the predicted probability of daily maximum 8-h-average ozone exceeding a standard value by 1) estimating climatological relative frequency, 2) establishing a probability of an exceedance threshold as 50%, 3) maximizing the threat score, and 4) determining the unit bias ratio. REGiS is trained using 2000–11 ozone-season (1 May–30 September) data, calibrated using 2012–14 data, and evaluated using 2015–18 data. Assessment of the calibration data with the Pierce skill score suggests an exceedance threshold based on climatological relative frequency for the conversion from probabilistic to deterministic forecasts. Calibrated REGiS generally compares well to predictions from the U.S. national air quality model and operational “expert” forecasts over the evaluation period. For other probabilistic models and situations, different procedures of converting probabilistic to deterministic forecasts may be more beneficial. The methods presented in this paper represent an approach for operational air quality forecasters seeking to use probabilistic model output to support forecasts designed to protect public health.

Nikolay Balashov↗