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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS1993

Validation of ISCCP cloud detections

This paper describes the validation of the International Satellite Cloud Climatology project (ISCCP)cloud detections by verifying the accuracy of the infrared clear-sky radiances. Comparison of retrieved surface temperatures to other measurements shows that bias errors are less than 2 K and random errors are about 2 K for sea surface (monthly means at 280-km scales) and that bias errors are less than 2 K and random errors are about 4 K for land surfaces (3 hourly at 280-km scales). Bias errors over a few persistently cloudy locations are sometimes -(2-4) K and over winter sea ice may be about +2 K. Surface reflectances are confirmed to be within 3% of other measurements and models for ocean, except for sun glint geometries, and to be within 3%-5% for land surfaces. Sufficiently accurate validation data are not avilable for visible reflectances of sea ice and snow-covered land, but some tests of specific cases suggest that errors are approximately 10%. These errors in clear-sky radiances suggest uncertainties in the ISCCP cloud detections of about 10% with a small (3%-6%) negative bias over land. Some specific regions exhibit both larger rms uncertainties and somewhat larger biases in cloud amount approaching 10%. ISCCP cloud detections are more in error over the polar regions than anywhere else. Based on comparisons with an anlysis of radiances measured at other wavelengths, the ISCCP analysis appears to miss 15%-25% of the clouds in summer but only 5%-10% of the winter clouds.

Rossow, William B.↗
NASA NTRSDate not supplied

Earth Radiation Budget Climate Record Composed of Multiple Satellite Observations

Effects of cloud diurnal cycle on top-of-atmosphere (TOA) and surface regional monthly mean irradiances, climatological mean, and anomalies are analyzed using CERES derived TOA irradiances and surface irradiances computed with MODIS derived cloud properties. Cloud properties derived from Terra and Aqua MODIS are sufficient to capture cloud diurnal cycle to compute regional monthly mean surface irradiances. While missing cloud diurnal cycle leads to a biased TOA and surface regional irradiances for regions with a strong cloud diurnal cycle, monthly regional TOA and surface anomalies derived from one sun-synchronous orbit agrees well with those derived from two sun-synchronous orbits. Based on these results, the algorithm to produce Edition 4.2 CERES EBAF product is developed. Regional TOA and surface climatological means derived from one sun-synchronous orbit are adjusted to match corresponding climatological means derived from Terra+Aqua observations. This climatological adjustment approach is used to merge the Terra only period to the Terra+Aqua period and to extend the Terra and Aqua record by merging NOAA20 observations. Two additional differences of Edition 4.2 EBAF algorithm to compute surface irradiances compared to the earlier version are: 1) no geostationary satellite derived cloud properties are used and 2) temperature and humidity from MERRA-2 instead of GEOIS-5.4.1 are used. Once surface monthly regional mean irradiances are compared with surface observations, the agreement is equivalent to the agreement with the earlier version. However, because surface irradiances are not affected by geostationary satellite artifacts, regional surface irradiance anomaly time series is significantly improved, especially for longwave irradiances.

Seiji Kato↗
NASA NTRS2013

Intercomparison of Satellite Dust Retrieval Products over the West African Sahara During the Fennec Campaign in June 2011

Dust retrievals over the Sahara Desert during June 2011 from the IASI, MISR, MODIS, and SEVIRI satellite instruments are compared against each other in order to understand the strengths and weaknesses of each retrieval approach. Particular attention is paid to the effects of meteorological conditions, land surface properties, and the magnitude of the dust loading. The period of study corresponds to the time of the first Fennec intensive measurement campaign, which provides new ground-based and aircraft measurements of the dust characteristics and loading. Validation using ground-based AERONET sunphotometer data indicate that of the satellite instruments, SEVIRI is most able to retrieve dust during optically thick dust events, whereas IASI and MODIS perform better at low dust loadings. This may significantly affect observations of dust emission and the mean dust climatology. MISR and MODIS are least sensitive to variations in meteorological conditions, while SEVIRI tends to overestimate the aerosol optical depth (AOD) under moist conditions (with a bias against AERONET of 0.31), especially at low dust loadings where the AOD<1. Further comparisons are made with airborne LIDAR measurements taken during the Fennec campaign, which provide further evidence for the inferences made from the AERONET comparisons. The effect of surface properties on the retrievals is also investigated. Over elevated surfaces IASI retrieves AODs which are most consistent with AERONET observations, while the AODs retrieved by MODIS tend to be biased low. In contrast, over the least emissive surfaces IASI significantly underestimates the AOD (with a bias of -0.41), while MISR and SEVIRI show closest agreement.

Fennec Campaign↗
NASA NTRS2017

Effects of Surface BRDF on the OMI Cloud and NO2 Retrievals: A New Approach Based on Geometry-Dependent Lambertian Equivalent Reflectivity (GLER) Derived from MODIS

The Ozone Monitoring Instrument (OMI) cloud and NO2 algorithms use a monthly gridded surface reflectivity climatology that does not depend upon the observation geometry. In reality, reflection of incoming direct and diffuse solar light from land or ocean surfaces is sensitive to the sun sensor geometry. This dependence is described by the bidirectional reflectance distribution function (BRDF). To account for the BRDF, we propose to use a new concept of geometry-dependent Lambertian equivalent reflectivity (GLER). Implementation within the existing OMI cloud and NO2 retrieval infrastructure requires changes only to the input surface reflectivity database. GLER is calculated using a vector radiative transfer model with high spatial resolution BRDF information from MODIS over land and the Cox Munk slope distribution over ocean with a contribution from water-leaving radiance. We compare GLER and climatological LER at 466 nm, which is used in the OMI O2-O2cloud algorithm to derive effective cloud fractions. A detailed comparison of the cloud fractions and pressures derived with climatological and GLERs is carried out. GLER and corresponding retrieved cloud products are then used as input to the OMI NO2 algorithm. We find that replacing the climatological OMI-based LERs with GLERs can increase NO2 vertical columns by up to 50 % in highly polluted areas; the differences include both BRDF effects and biases between the MODIS and OMI-based surface reflectance data sets. Only minor changes to NO2 columns (within 5 %) are found over unpolluted and overcast areas.

MODIS BRDF OMI clouds NO2 GLER↗
NASA NTRS2019

A Double Bootstrap Approach to Superposed Epoch Analysis to Evaluate Response Uncertainty

The association between climate variability and episodic events, such as the antecedent moisture conditions prior to wildfire or the cooling following volcanic eruptions, is commonly assessed using Superposed Epoch Analysis (SEA). In SEA the epochal response is typically calculated as the average climate conditions prior to and following all event years or their deviation from climatology. However, the magnitude and significance of the inferred climate association may be sensitive to the selection or omission of individual key years, potentially resulting in a biased assessment of the relationship between these events and climate. Here we describe and test a modified double-bootstrap SEA that generates multiple unique draws of the key years and evaluates the sign, magnitude, and significance of event-climate relationships within a probabilistic framework. This multiple resampling helps quantify multiple uncertainties inherent in conventional applications of SEA within dendrochronology and paleoclimatology. We demonstrate our modified SEA by evaluating the volcanic cooling signal in a Northern Hemisphere tree-ring temperature reconstruction and the link between drought and wildfire events in the western United States. Finally, we make our Matlab and R code available to be adapted for future SEA applications.

Dendroclimatology↗
NASA NTRS1999

Climatological Structures of the GRIPS Models: Mean States and Forcing

The GCM-Reality Intercomparison Project for SPARC (GRIPS) is assessing and monitoring the performance of state-of-the-art general circulation models (GCMs). A wide variety of tasks have been initiated. These are designed to: (1) assess the ability of the GCMs to represent the current climatological structure of the troposphere and middle atmosphere,(2) to compare their response to imposed forcing anomalies, and (3) to estimate the certainty with which future climate perturbations can be predicted. This paper is concerned with assessments of the climatological states in the GCM simulations. Comparing the simulations with observational datasets reveals considerable discrepancies in the modelled fields. While it might be anticipated that certain types of biases in the model simulations might be related to the formulation of different aspects of the numerical package (dynamical schemes, cloud schemes, radiation transfer, inclusion of gravity wave drag), there is no clear relationship between these features. This paper attempts to draw a more comprehensive picture of the GCMs'performance than has previously been shown, by comparing the dominant forcing mechanisms in the models with observational estimates, and relating model deficiencies to the differences in the physical mechanisms in the GCMS.

Pawson, Steven↗
NASA NTRS2002

Intercomparison of Model Simulations of the Impact of 1997/98 El Nino on South American Summer Monsoon

The simulations of climatology and response of the South American summer monsoon (SASM) to the 1997/98 El Nino are investigated using six atmospheric general circulation models. Results show all models simulate the large-scale features of the SASM reasonably well. However, both stationary and seasonal components of the surface pressure are overestimated, resulting in an excessively strong SASM in the model climatology. The low-level northwesterly jet over eastern foothills of the Andes is not well resolved because of the coarse resolution of the models. Large rainfall simulation biases are found in association with the Andes and the Atlantic ITCZ, indicating model problems in handling steep mountains and parameterization of convective processes. The simulation of the 1997/98 El Nino impact on SASM is examined based on an ensemble of ten two-year (September 1996 - August 1998) integration. Results show that most models can simulate the large-scale tropospheric warming response over the tropical central Pacific, including the dynamic response of Rossby wave propagation of the Pacific-South America (PSA) pattern that influences remote areas. Deficiencies are found in simulating the regional impacts over South America. Model simulation fails to capture the southeastward expansion of anomalously warm tropospheric air. As a result, the upper tropospheric anomalous high over the subtropical Andes is less pronounced, and the enhancement of subtropical westerly jet is displaced 5deg-10deg equatorward compared to the observed. Over the Amazon basin, the shift of Walker cell induced by El Nino is not well represented, showing anomalous easterlies in both upper and lower troposphere.

Zhou, Jiayu↗
NASA NTRSDate not supplied

Examination of L-Band Brightness Temperature Forecasts in the SMAP Level-4 Soil Moisture Analysis

The NASA Soil Moisture Active Passive (SMAP) mission [4] has been providing L-band (1.4 GHz) passive microwave brightness temperature (Tb) observations since April 2015. By assimilating the Tb observations into the NASA Catchment land surface model [5] using a spatially distributed ensemble Kalman filter, the NASA Global Modeling and Assimilation Office generates the SMAP Level-4 Soil Moisture (L4_SM) product, which provides global, 3-hourly, 9-km resolution estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture with ~2.5-day latency for use in research and applications [6]. The L4_SM product also includes estimates of soil temperature, land surface fluxes, and assimilation diagnostics such as the model forecast and observed Tb values [7]. The output from the L4_SM system is routinely monitored by the L4_SM team. Such monitoring provides valuable information; instances of unusually large Tb observation-minus-forecast (O-F) residuals can indicate events during which soil moisture conditions are poorly described in the land modeling system [7,8]. For example, repeated occurrences of very large Tb O-F values in central Australia were traced back to deficiencies in the gauge-based precipitation product used in the land modeling system through L4_SM Version 5; this discovery prompted the use, beginning in Version 6, of satellite- and gauge-based precipitation observations outside of North America [9]. Recently, a statistical analysis of the Tb O-F residuals revealed systematic errors in the “tau-omega” L-band radiative transfer model that converts the land-model simulated soil moisture and temperature into the forecast Tb prior to the L4_SM analysis. The L4_SM analysis is built on the ensemble Kalman filter and assumes unbiased forecast errors. The presence of systematic Tb forecast errors could thus adversely impact the quality of the analyzed soil moisture. In this paper, we examine the Tb O-F residuals of the latest Version 7 L4_SM data (Science Version ID Vv7030 and Vv7032) [10,11,12]. In the L4_SM Version 7 algorithm, key parameters of the L-band radiative transfer model, including soil roughness, scattering albedo, and a (seasonally varying) climatology of vegetation opacity, are obtained from the SMAP Level-2 Radiometer retrieval product (Version 5) [1]. Additionally, we also use ground measurements of surface soil moisture and soil temperature from the SMAP core validation sites as in situ reference of soil conditions [2,3]. As part of the L4_SM system calibration, the seasonally-varying bias between the model forecast Tb and the observed values is removed prior to the assimilation of the SMAP Tb observations [7]. That is, the L4_SM system is designed to only correct errors in synoptic-scale and interannual variations from the long-term mean seasonal cycle while maintaining the model’s (potentially erroneous) climatology. The Tb O-F residuals examined here are thus computed using SMAP Tb observations after they are rescaled to the mean seasonal cycle of Tb from the modeling system without SMAP data assimilation. Consequently, the long-term average of the Tb O-F residuals has a global mean of only 0.13 K and locally small values, ranging from -1 to 3 K. Despite the small time-average values of the Tb O-F residuals, the model forecast Tb was nevertheless found to exhibit undesirable systematic errors. At some locations, the time-average Tb O-F values strongly depend on surface soil moisture (SM). At the Yanco SMAP core validation site, for example, the Tb O-F residuals typically range from 5 to 15 K under dry soil moisture conditions (SM < 0.15 m3 m-3) yet are predominantly negative under wet soil moisture conditions (SM > 0.25 m3 m-3), with values ranging from 0 to -40 K. This results in soil moisture analysis increments that persistently make the soil drier under dry SM conditions and persistently make the soil wetter under wet SM conditions, suggesting an error in the dynamic range of the simulated Tb, soil moisture or soil temperature. In this paper, we describe the higher-order systematic Tb forecast errors in more detail, examine their impact on the L4_SM product quality, and explore potential avenues to further improve the L4_SM algorithm.

Rolf Reichle↗
NASA NTRS2011

Diagnosing AIRS Sampling with CloudSat Cloud Classes

AIRS yield and sampling vary with cloud state. Careful utilization of collocated multiple satellite sensors is necessary. Profile differences between AIRS and ECMWF model analyses indicate that AIRS has high sampling and excellent accuracy for certain meteorological conditions. Cloud-dependent sampling biases may have large impact on AIRS L2 and L3 data in climate research. MBL clouds / lower tropospheric stability relationship is one example. AIRS and CloudSat reveal a reasonable climatology in the MBL cloud regime despite limited sampling in stratocumulus. Thermodynamic parameters such as EIS derived from AIRS data map these cloud conditions successfully. We are working on characterizing AIRS scenes with mixed cloud types.

model analyses↗
NASA NTRS2013

Connections Between the Spring Breakup of the Southern Hemisphere Polar Vortex, Stationary Waves, and Air-sea Roughness

A robust connection between the drag on surface-layer winds and the stratospheric circulation is demonstrated in NASA's Goddard Earth Observing System Chemistry-Climate Model (GEOSCCM). Specifically, an updated parameterization of roughness at the air-sea interface, in which surface roughness is increased for moderate wind speeds (4ms to 20ms), leads to a decrease in model biases in Southern Hemispheric ozone, polar cap temperature, stationary wave heat flux, and springtime vortex breakup. A dynamical mechanism is proposed whereby increased surface roughness leads to improved stationary waves. Increased surface roughness leads to anomalous eddy momentum flux convergence primarily in the Indian Ocean sector (where eddies are strongest climatologically) in September and October. The localization of the eddy momentum flux convergence anomaly in the Indian Ocean sector leads to a zonally asymmetric reduction in zonal wind and, by geostrophy, to a wavenumber-1 stationary wave pattern. This tropospheric stationary wave pattern leads to enhanced upwards wave activity entering the stratosphere. The net effect is an improved Southern Hemisphere vortex: the vortex breaks up earlier in spring (i.e., the spring late-breakup bias is partially ameliorated) yet is no weaker in mid-winter. More than half of the stratospheric biases appear to be related to the surface wind speed biases. As many other chemistry climate models use a similar scheme for their surface layer momentum exchange and have similar biases in the stratosphere, we expect that results from GEOSCCM may be relevant for other climate models.

air-sea interaction↗
NASA NTRS1984

Recent advances in multispectral sensing of ocean surface temperature from space

Visual and infrared measurements from the five channel AVHRR on the NOAA-7 satellite are used operationally to derive sea surface temperatures. The multichannel data perform daytime and nighttime cloud detection tests, and the several atmospheric window channels in the thermal infrared correct for atmospheric attenuation. Monitoring of the sea surface temperature product with buoy data indicates stability in mean bias and rms difference with little variation by season or geographic area. Global mapping enables the derivation of monthly mean isotherms, monthly and annual changes, and anomaly patterns relative to climatology. Problems are associated with noise in the 3.7 micro m window channel, and with the injection of substantial volcanic aerosol into the stratosphere by the El Chichon eruption. Multichannel sea surface temperature charts are used to study phenomena such as equatorial long waves and the recent El Nino episode.

Mcclain, E. P.↗
NASA NTRS2017

Accounting for the Effects of Surface BRDF on Satellite Cloud and Trace-Gas Retrievals: A New Approach Based on Geometry-Dependent Lambertian-Equivalent Reflectivity Applied to OMI Algorithms

Most satellite nadir ultraviolet and visible cloud, aerosol, and trace-gas algorithms make use of climatological surface reflectivity databases. For example, cloud and NO2 retrievals for the Ozone Monitoring Instrument (OMI) use monthly gridded surface reflectivity climatologies that do not depend upon the observation geometry. In reality, reflection of incoming direct and diffuse solar light from land or ocean surfaces is sensitive to the sun-sensor geometry. This dependence is described by the bidirectional reflectance distribution function (BRDF). To account for the BRDF, we propose to use a new concept of geometry-dependent Lambertian equivalent reflectivity (LER). Implementation within the existing OMI cloud and NO2 retrieval infrastructure requires changes only to the input surface reflectivity database. The geometry-dependent LER is calculated using a vector radiative transfer model with high spatial resolution BRDF information from the Moderate Resolution Imaging Spectroradiometer (MODIS) over land and the Cox-Munk slope distribution over ocean with a contribution from water-leaving radiance. We compare the geometry-dependent and climatological LERs for two wavelengths, 354 and 466 nm, that are used in OMI cloud algorithms to derive cloud fractions. A detailed comparison of the cloud fractions and pressures derived with climatological and geometry-dependent LERs is carried out. Geometry-dependent LER and corresponding retrieved cloud products are then used as inputs to our OMI NO2 algorithm. We find that replacing the climatological OMI-based LERs with geometry-dependent LERs can increase NO2 vertical columns by up to 50% in highly polluted areas; the differences include both BRDF effects and biases between the MODIS and OMI-based surface reflectance data sets. Only minor changes to NO2 columns (within 5 %) are found over unpolluted and overcast areas.

Lambert-equivalent reflectivity↗
NASA NTRS2014

Using Ensemble Short-Term Initialized Coupled NASA GEOS5 Climate Model Integrations to Study Convective Bias Growth

The representation of convective processes, particularly deep convection in the tropics, remains a persistent problem in climate models. In fact structural biases in the distribution of tropical rainfall in the CMIP5 models is hardly different than that of the CMIP3 versions. Given that regional climate change at higher latitudes is sensitive to the configuration of tropical forcing, this persistent bias is a major issue for the credibility of climate change projections. In this study we use model output from integrations of the NASA Global Earth Observing System Five (GEOS5) climate modeling system to study the evolution of biases in the location and intensity of convective processes. We take advantage of a series of hindcast experiments done in support of the US North American Multi-Model Ensemble (NMME) initiative. For these experiments a nine-month forecast using a coupled model configuration is made approximately every five days over the past 30 years. Each forecast is started with an updated analysis of the ocean, atmosphere and land states. For a given calendar month we have approximately 180 forecasts with daily means of various quantities. These forecasts can be averaged to essentially remove "weather scales" and highlight systematic errors as they evolve. Our primary question is to ask how the spatial structure of daily mean precipitation over the tropics evolves from the initial state and what physical processes are involved. Errors in parameterized convection, various water and energy fluxes and the divergent circulation are found to set up on fast time scales (order five days) compared to errors in the ocean, although SST changes can be non-negligible over that time. For the month of June the difference between forecast day five versus day zero precipitation looks quite similar to the difference between the June precipitation climatology and that from the Global Precipitation Climatology Project (GPCP). We focus much of our analysis on the influence of SST gradients, associated PBL baroclinicity enabled by turbulent mixing, the ensuing PBL moisture convergence, and how changes in these processes relate to convective precipitation bias growth over this short period.

Cohen, Charlie↗
DOE OSTI2024

Assessing Spatial Representativeness of Global Flux Tower Eddy-Covariance Measurements Using Data from FLUXNET2015

Large datasets of carbon dioxide, energy, and water fluxes were measured with the eddy-covariance (EC) technique, such as FLUXNET2015. These datasets are widely used to validate remote-sensing products and benchmark models. One of the major challenges in utilizing EC-flux data is determining the spatial extent to which measurements taken at individual EC towers reflect model-grid or remote sensing pixels. To minimize the potential biases caused by the footprint-to-target area mismatch, it is important to use flux datasets with awareness of the footprint. This study analyze the spatial representativeness of global EC measurements based on the open-source FLUXNET2015 data, using the published flux footprint model (SAFE-f). The calculated annual cumulative footprint climatology (ACFC) was overlaid on land cover and vegetation index maps to create a spatial representativeness dataset of global flux towers. The dataset includes the following components: (1) the ACFC contour (ACFCC) data and areas representing 50%, 60%, 70%, and 80% ACFCC of each site, (2) the proportion of each land cover type weighted by the 80% ACFC (ACFCW), (3) the semivariogram calculated using Normalized Difference Vegetation Index (NDVI) considering the 80% ACFCW, and (4) the sensor location bias (SLB) between the 80% ACFCW and designated areas (e.g. 80% ACFCC and window sizes) proxied by NDVI. Finally, we conducted a comprehensive evaluation of the representativeness of each site from three aspects: (1) the underlying surface cover, (2) the semivariogram, and (3) the SLB between 80% ACFCW and 80% ACFCC, and categorized them into 3 levels. The goal of creating this dataset is to provide data quality guidance for international researchers to effectively utilize the FLUXNET2015 dataset in the future.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

Ionospheric Signatures in Radio Occultation Data

We can extend robustly the radio occultation data record by 6 years (+60%) by developing a singlefrequency processing method for GPS/MET data. We will produce a calibrated data set with profile-byprofile data characterization to determine robust upper bounds on ionospheric bias. Part of an effort to produce a calibrated RO data set addressing other key error sources such as upper boundary initialization. Planned: AIRS-GPS water vapor cross validation (water vapor climatology and trends).

radio occultation↗
DOE OSTI2023

Combining regional mesh refinement with vertically enhanced physics to target marine stratocumulus biases as demonstrated in the Energy Exascale Earth System Model version 1

Abstract. In this paper we develop a novel framework aimed to significantly reduce biases related to marine stratocumulus clouds in general circulation models (GCMs) while circumventing excessive computational cost requirements. Our strategy is to increase the horizontal resolution using a regionally refined mesh (RRM) over our region of interest in addition to using the Framework for Improvement by Vertical Enhancement (FIVE) to increase the vertical resolution only for specific physical processes that are important for stratocumulus. We apply the RRM off the coast of Peru in the southeastern Pacific, a region that climatologically contains the most marine stratocumulus in the subtropics. We find that our new modeling framework is able to replicate the results of our high-resolution benchmark simulation with much fidelity, while reducing the computational cost by several orders of magnitude. In addition, this framework is able to greatly reduce the long-standing biases associated with marine stratocumulus in GCMs when compared to the standard-resolution control simulation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2013

First Evaluation of the Climatological Calibration Algorithm in the Real-time TMPA Precipitation Estimates over Two Basins at High and Low Latitudes

The TRMM Multi-satellite Precipitation Analysis (TMPA) system underwent a crucial upgrade in early 2009 to include a climatological calibration algorithm (CCA) to its realtime product 3B42RT, and this algorithm will continue to be applied in the future Global Precipitation Measurement era constellation precipitation products. In this study, efforts are focused on the comparison and validation of the Version 6 3B42RT estimates before and after the climatological calibration is applied. The evaluation is accomplished using independent rain gauge networks located within the high-latitude Laohahe basin and the low-latitude Mishui basin, both in China. The analyses indicate the CCA can effectively reduce the systematic errors over the low-latitude Mishui basin but misrepresent the intensity distribution pattern of medium-high rain rates. This behavior could adversely affect TMPA's hydrological applications, especially for extreme events (e.g., floods and landslides). Results also show that the CCA tends to perform slightly worse, in particular, during summer and winter, over the high-latitude Laohahe basin. This is possibly due to the simplified calibration-processing scheme in the CCA that directly applies the climatological calibrators developed within 40 degrees latitude to the latitude belts of 40 degrees N-50 degrees N. Caution should therefore be exercised when using the calibrated 3B42RT for heavy rainfall-related flood forecasting (or landslide warning) over high-latitude regions, as the employment of the smooth-fill scheme in the CCA bias correction could homogenize the varying rainstorm characteristics. Finally, this study highlights that accurate detection and estimation of snow at high latitudes is still a challenging task for the future development of satellite precipitation retrievals.

TMPA-RT Precipitation Estimates↗
NASA NTRS2014

Global Patterns of Lightning Properties Derived by OTD and LIS

The satellite instruments Optical Transient Detector (OTD) and Lightning Imaging Sensor (LIS) provide unique empirical data about the frequency of lightning flashes around the globe (OTD), and the tropics (LIS), which 5 has been used before to compile a well received global climatology of flash rate densities. Here we present a statistical analysis of various additional lightning properties derived from OTD/LIS, i.e. the number of so-called "events" and "groups" per flash, as well as 10 the mean flash duration, footprint and radiance. These normalized quantities, which can be associated with the flash "strength", show consistent spatial patterns; most strikingly, oceanic flashes show higher values than continental flashes for all properties. Over land, regions with high (Eastern US) 15 and low (India) flash strength can be clearly identified. We discuss possible causes and implications of the observed regional differences. Although a direct quantitative interpretation of the investigated flash properties is difficult, the observed spatial patterns provide valuable information for the 20 interpretation and application of climatological flash rates. Due to the systematic regional variations of physical flash characteristics, viewing conditions, and/or measurement sensitivities, parametrisations of lightning NOx based on total flash rate densities alone are probably affected by regional biases.

Beirle, Steffen↗