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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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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

A-Train Observations of Deep Convective Storm Tops

The paper highlights simultaneous observations of tops of deep convective clouds from several space-borne instruments including the Moderate Resolution Imaging Spectroradiometer (MODIS) of the Aqua satellite, Cloud Profiling Radar (CPR) of the CloudSat satellite, and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) flown on the CALIPSO satellite. These satellites share very close orbits, thus together with several other satellites they are referred to as the "A-Train" constellation. Though the primary responsibility of these satellites and their instrumentation is much broader than observations of fine-scale processes atop convective storms, in this study we document how data from the A-Train can contribute to a better understanding and interpretation of various storm-top features, such as overshooting tops, cold-U/V and cold ring features with their coupled embedded warm areas, above anvil ice plumes and jumping cirrus. The relationships between MODIS multi-spectral brightness temperature difference (BTD) fields and cloud top signatures observed by the CPR and CALIOP are also examined in detail to highlight the variability in BTD signals across convective storm events.

Setvak, Martin↗
NASA NTRS2012

CloudSat Anomaly Recovery and Operational Lessons Learned

In April 2011, NASA's pioneering cloud profiling radar satellite, CloudSat, experienced a battery anomaly that placed it into emergency mode and rendered it operations incapable. All initial attempts to recover the spacecraft failed as the resultant power limitations could not support even the lowest power mode. Originally part of a six-satellite constellation known as the "A-Train", CloudSat was unable to stay within its assigned control box, posing a threat to other A-Train satellites. CloudSat needed to exit the constellation, but with the tenuous power profile, conducting maneuvers was very risky. The team was able to execute a complex sequence of operations which recovered control, conducted an orbit lower maneuver, and returned the satellite to safe mode, within one 65 minute sunlit period. During the course of the anomaly recovery, the team developed several bold, innovative operational strategies. Details of the investigation into the root-cause and the multiple approaches to revive CloudSat are examined. Satellite communication and commanding during the anomaly are presented. A radical new system of "Daylight Only Operations" (DO-OP) was developed, which cycles the payload and subsystem components off in tune with earth eclipse entry and exit in order to maintain positive power and thermal profiles. The scientific methodology and operational results behind the graduated testing and ramp-up to DO-OP are analyzed. In November 2011, the CloudSat team successfully restored the vehicle to consistent operational collection of cloud radar data during sunlit portions of the orbit. Lessons learned throughout the six-month return-to-operations recovery effort are discussed and offered for application to other R&D satellites, in the context of on-orbit anomaly resolution efforts.

cloud profiling radar satellite↗
NASA NTRS2012

Validation of Satellite-Based Objective Overshooting Cloud-Top Detection Methods Using CloudSat Cloud Profiling Radar Observations

Two satellite infrared-based overshooting convective cloud-top (OT) detection methods have recently been described in the literature: 1) the 11-mm infrared window channel texture (IRW texture) method, which uses IRW channel brightness temperature (BT) spatial gradients and thresholds, and 2) the water vapor minus IRW BT difference (WV-IRW BTD). While both methods show good performance in published case study examples, it is important to quantitatively validate these methods relative to overshooting top events across the globe. Unfortunately, no overshooting top database currently exists that could be used in such study. This study examines National Aeronautics and Space Administration CloudSat Cloud Profiling Radar data to develop an OT detection validation database that is used to evaluate the IRW-texture and WV-IRW BTD OT detection methods. CloudSat data were manually examined over a 1.5-yr period to identify cases in which the cloud top penetrates above the tropopause height defined by a numerical weather prediction model and the surrounding cirrus anvil cloud top, producing 111 confirmed overshooting top events. When applied to Moderate Resolution Imaging Spectroradiometer (MODIS)-based Geostationary Operational Environmental Satellite-R Series (GOES-R) Advanced Baseline Imager proxy data, the IRW-texture (WV-IRW BTD) method offered a 76% (96%) probability of OT detection (POD) and 16% (81%) false-alarm ratio. Case study examples show that WV-IRW BTD.0 K identifies much of the deep convective cloud top, while the IRW-texture method focuses only on regions with a spatial scale near that of commonly observed OTs. The POD decreases by 20% when IRW-texture is applied to current geostationary imager data, highlighting the importance of imager spatial resolution for observing and detecting OT regions.

Bedka, Kristopher M.↗
NASA NTRS2008

Seeking Augmented Information Content Concerning Diurnal Precipitation Achieved by Combining TRMM-PR and CloudSat-CPR Radar Data Sets

The CloudSat satellite's Cloud Profiling Radar (CPR) is a highly sensitive 94 GHz (W-band) nadir viewing radar system flown in retrograde sun synchronous orbit useful for determining the vertical structure of cloud hydrometeors down to sensitivity of approx. -30 dBZ reflectivity factor. Given this sensitivity, it is possible to unambiguously measure precipitation rates in clouds over a spectrum extending from approx. 0.08 - 3.0 mm hr (sup -1) down to altitudes of 0.5 km with approx.0.25 km vertical binning. This enables an effective means to measure a great deal of the drizzle and light rain spectrum. However, because of its near-polar sunsynchronous orbit, CloudSat cannot sample the diurnal cycle of precipitation, nor with its nadir-only CPR view can it obtain a high duty cycle in sampling precipitation at fixed local times over fixed positions. On the other hand, the TRMM satellite, which is flown in a non-sunsynchronous 35-degree inclined orbit carrying the 13.8 GHz KU-band Precipitation Radar (PR) scanning through nadir over an approx. 225 km swath, can sample both the diurnal cycle and with a much improved duty cycle relative to CloudSat. Moreover, the PR and CPR have the same 0.25 km vertical binning capability. The PR's greatest shortcoming is its approx. +17 dBZ sensitivity, which eliminates the possibility of measuring rain rates below -0.3-0.5 mm hr(sup -1), which can involve rainfall accumulations of up to 50% of the total over some regions. This begs the question of whether by combining CPR and PR data sets, whether it is possible to obtain an augmented measurement of the diurnal precipitation cycle. By collecting complimentary datasets during CloudSat and TRMM satellite orbit crossings within a delta t = 45-min proximity window, it is possible to demonstrate that whenever TRMM detects a precipitation signal, the correlations along the vertical axis between the reflectivities acquired from the CPR and PR are in inverse proportion to the magnitude of the delta t proximity window. By taking advantage of these underlying correlations, it is possible to develop a functional which can be used to broaden the reflectivity spectrum, concomitantly the rain rate spectrum, of the PR measurements based on the inherently broader reflectivity spectrum of the CPR at the lower reflectivity end of the spectrum, concomitantly at lighter rain rates. With the functional in place, it is then possible to produce synthetic CloudSat precipitation imagery over the PR track and thus over the diurnal time period. These augmented data are then used to study the spectral-vertical diurnal properties of precipitation over oceanic regions observed by TRMM.

Smith, Eric A.↗
NASA NTRS2005

Cloud profiling radar for the CloudSat Mission

The CloudSat Mission is a new satellite mission jointly developed by NASA, JPL, the Canadian Agency, Colorado State University, and the US AirForce to acquire a global data set of vertical cloud structure and its variability.

vertical profiling↗
NASA NTRSDate not supplied

TPSAS-NF1676L-29044-DND

In Langley NASA, Clouds and the Earth’s Radiant Energy System (CERES) and Moderate Resolution Imaging Spectroradiometer (MODIS) are merged with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and CloudSat Cloud Profiling Radar (CPR). The CERES merged product (C3M) matches up to three CALIPSO footprints with each MODIS pixel along its ground track. It then assigns the nearest CloudSat footprint to each of those MODIS pixels. The cloud properties from MODIS, retrieved using the CERES algorithms, are included in C3M with the matched CALIPSO and CloudSat products along with radiances from 18 MODIS channels. The dataset is used to validate the CERES retrieved MODIS cloud properties and the computed TOA and surface flux difference using MODIS or CALIOP/CloudSAT retrieved clouds. This information is then used to tune the computed fluxes to match the CERES observed TOA flux. The capability of viewing C3M product on browsers will be available on CERES ordering tool web site in the future, https://ceres.larc.nasa.gov

David R Doelling↗
NASA NTRS2008

Current Scientific Progress and Future Scientific Prospects Enabled by Spaceborne Precipitation Radar Measurements

First, we examine current scientific progress and understanding that have been possible through use of spaceborne precipitation radar measurements being provided by the TRMM and CloudSat satellites. Second, we look across a future 20-year time frame to assess how and why anticipated improvements in space radar systems will further advance scientific progress into topic areas once considered beyond the realm of space-based remote sensing. JAXA's 13.8 GHz Ku-band cross-track scanning Precipitation Radar (PR) developed for flight on NASA's non-sun-synchronous, diurnally-precessing TRMM satellite, was the first Earth radar flown in space that was designed specifically for precipitation measurement. Its proven accuracy in measuring global rainfall in the tropics and sub-tropics and its unanticipated longevity in continuing these measurements beyond a full decade have established the standards against which all follow-up and future space radars will be evaluated. In regards to the current PR measurement time series, we will discuss a selection of major scientific discoveries and impacts which have set the stage for future radar measuring systems. In fact, the 2nd contemporary space radar applicable for terrestrial precipitation measurement, i.e., JPL-CSA's 94 GHz nadir-staring Cloud Profiling Radar (CPR) flown on NASA's sun-synchronous CloudSat satellite, although designed primarily for measurement of non-precipitating cloud hydrometeors and aerosols, has also unquestionably advanced precipitation measurement because CPR's higher frequency and greatly increased sensitivity (approximately 30 dBZ) has enabled global observations of light rain rate spectrum processes (i.e., rain rates below 0.05 mm per hourand of precipitation processes in the high troposphere (particularly ice phase processes). These processes are beyond reach of the TRMM radar because the PR sensitivity limit is approximately 17 dBZ which means its lower rain rate cutoff is around 0.3 mm per hour and its vertical profiling acuity is greatly limited above the melting layer. Thus, the newer CPR measurements have become important for a variety of scientific reasons that will be highlighted and assessed. In considering scientific progress likely to stem from future precipitation radar systems, we will specifically examine possible scientific impacts from three anticipated missions for which NASA and various of its space agency partners are expected to lead the way. These three missions are: (1) the nearterm Global Precipitation Measuring (GPM) Mission; (2) the decadal timeline Aerosol and Cloud Experiment (ACE) Mission; and the post-decadal timeline NEXRAD in Space (NIS) Mission. The observational capabilities of the planned radar systems for each of these three satellite missions are distinct from each other and each provides progressive improvements in precipitation measuring and scientific research capabilities relative to where we are now -- insofar as TRMM PR and the CloudSat CPR capabilities. The potential innovations in scientific research will be discussed in a framework of likely synergisms between next-generation radar capabilities and accessible dynamical and microphysical properties that have heretofore evaded detection.

Smith, Eric A.↗
DOE OSTI2026

First EarthCARE CPR Observations of Dynamics–Microphysics Coupling in Marine Cold‐Air Outbreak Clouds

Marine cold-air outbreak (MCAO) clouds begin as shallow cloud streets near the ice edge and evolve into broken open-cell convection downstream. Their rapid transitions in cloud structure and microphysics are closely linked to vertical motions, but this interplay has not previously been observed from space. The EarthCARE satellite, carrying the first spaceborne Doppler radar (Cloud Profiling Radar, CPR), now measures hydrometeor sedimentation velocity and vertical air motion. Using CPR observations over the Norwegian and Barents Seas (December 2024–May 2025), we analyze MCAO and non-MCAO clouds. We find that strong updrafts in MCAO clouds coincide with larger supercooled liquid water (SLW) path (LWP). These conditions are accompanied by increases in sedimentation velocity and its vertical gradient, reflecting rapid riming-driven ice growth. In contrast, non-MCAO clouds show lower riming efficiency at similar LWP due to relatively weaker dynamical support. This emphasizes the importance of dynamics in shaping polar cloud structure and microphysics.

Kim, Jiseob [McGill Univ., Montreal, QC (Canada)] ↗
NASA NTRSDate not supplied

Sea Salt and Dust Optical Depths in the Caribbean and Equatorial Western Atlantic: A Multi-technique Analysis

Many instruments and algorithms can estimate aerosol optical depth (AOD) of the atmosphere. The Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua spacecraft provides estimates at several wavelengths. The Synergized Optical Depth of Aerosols (SODA) algorithm utilizes the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) spacecraft and the Cloud Profiling Radar (CPR) onboard the CloudSat spacecraft’s ocean returns to estimate AOD. CALIOP routinely estimates AOD for all individual layers detected within a column and these can be summed to generate a total column approximation. With the release of the Version 4.51 Lidar Level 2 data products in October 2023, direct estimates of total column optical depth are now derived using the Ocean Derived Column Optical Depths (ODCOD) algorithm. In this poster we investigate the multispectral AOD estimates, and derived Angstrom exponents of marine sea salt and dust provided by MODIS, SODA, CALIOP, and ODCOD in a major dust transport and deposition region of the equatorial Atlantic Ocean.

optical depth↗
NASA NTRS2014

Introducing Multisensor Satellite Radiance-Based Evaluation for Regional Earth System Modeling

Earth System modeling has become more complex, and its evaluation using satellite data has also become more difficult due to model and data diversity. Therefore, the fundamental methodology of using satellite direct measurements with instrumental simulators should be addressed especially for modeling community members lacking a solid background of radiative transfer and scattering theory. This manuscript introduces principles of multisatellite, multisensor radiance-based evaluation methods for a fully coupled regional Earth System model: NASA-Unified Weather Research and Forecasting (NU-WRF) model. We use a NU-WRF case study simulation over West Africa as an example of evaluating aerosol-cloud-precipitation-land processes with various satellite observations. NU-WRF-simulated geophysical parameters are converted to the satellite-observable raw radiance and backscatter under nearly consistent physics assumptions via the multisensor satellite simulator, the Goddard Satellite Data Simulator Unit. We present varied examples of simple yet robust methods that characterize forecast errors and model physics biases through the spatial and statistical interpretation of various satellite raw signals: infrared brightness temperature (Tb) for surface skin temperature and cloud top temperature, microwave Tb for precipitation ice and surface flooding, and radar and lidar backscatter for aerosol-cloud profiling simultaneously. Because raw satellite signals integrate many sources of geophysical information, we demonstrate user-defined thresholds and a simple statistical process to facilitate evaluations, including the infrared-microwave-based cloud types and lidar/radar-based profile classifications.

Planetary Boundary Layer↗
NASA NTRS2009

Winter QPF Sensitivities to Snow Parameterizations and Comparisons to NASA CloudSat Observations

Steady increases in computing power have allowed for numerical weather prediction models to be initialized and run at high spatial resolution, permitting a transition from larger scale parameterizations of the effects of clouds and precipitation to the simulation of specific microphysical processes and hydrometeor size distributions. Although still relatively coarse in comparison to true cloud resolving models, these high resolution forecasts (on the order of 4 km or less) have demonstrated value in the prediction of severe storm mode and evolution and are being explored for use in winter weather events . Several single-moment bulk water microphysics schemes are available within the latest release of the Weather Research and Forecast (WRF) model suite, including the NASA Goddard Cumulus Ensemble, which incorporate some assumptions in the size distribution of a small number of hydrometeor classes in order to predict their evolution, advection and precipitation within the forecast domain. Although many of these schemes produce similar forecasts of events on the synoptic scale, there are often significant details regarding precipitation and cloud cover, as well as the distribution of water mass among the constituent hydrometeor classes. Unfortunately, validating data for cloud resolving model simulations are sparse. Field campaigns require in-cloud measurements of hydrometeors from aircraft in coordination with extensive and coincident ground based measurements. Radar remote sensing is utilized to detect the spatial coverage and structure of precipitation. Here, two radar systems characterize the structure of winter precipitation for comparison to equivalent features within a forecast model: a 3 GHz, Weather Surveillance Radar-1988 Doppler (WSR-88D) based in Omaha, Nebraska, and the 94 GHz NASA CloudSat Cloud Profiling Radar, a spaceborne instrument and member of the afternoon or "A-Train" of polar orbiting satellites tasked with cataloguing global cloud characteristics. Each system provides a unique perspective. The WSR-88D operates in a surveillance mode, sampling cloud volumes of Rayleigh scatterers where reflectivity is proportional to the sixth moment of the size distribution of equivalent spheres. The CloudSat radar provides enhanced sensitivity to smaller cloud ice crystals aloft, as well as consistent vertical profiles along each orbit. However, CloudSat reflectivity signatures are complicated somewhat by resonant Mie scattering effects and significant attenuation in the presence of cloud or rain water. Here, both radar systems are applied to a case of light to moderate snowfall within the warm frontal zone of a cold season, synoptic scale storm. Radars allow for an evaluation of the accuracy of a single-moment scheme in replicating precipitation structures, based on the bulk statistical properties of precipitation as suggested by reflectivity signatures.

Molthan, Andrew↗
NASA NTRS1993

Normalized vertical ice mass flux profiles from vertically pointing 8-mm-wavelength Doppler radar

During the FIRE 2 (First International Satellite Cloud Climatology Project Regional Experiment) project, NOAA's Wave Propagation Laboratory (WPL) operated its 8-mm wavelength Doppler radar extensively in the vertically pointing mode. This allowed for the calculation of a number of important cirrus cloud parameters, including cloud boundary statistics, cloud particle characteristic sizes and concentrations, and ice mass content (imc). The flux of imc, or, alternatively, ice mass flux (imf), is also an important parameter of a cirrus cloud system. Ice mass flux is important in the vertical redistribution of water substance and thus, in part, determines the cloud evolution. It is important for the development of cloud parameterizations to be able to define the essential physical characteristics of large populations of clouds in the simplest possible way. One method would be to normalize profiles of observed cloud properties, such as those mentioned above, in ways similar to those used in the convective boundary layer. The height then scales from 0.0 at cloud base to 1.0 at cloud top, and the measured cloud parameter scales by its maximum value so that all normalized profiles have 1.0 as their maximum value. The goal is that there will be a 'universal' shape to profiles of the normalized data. This idea was applied to estimates of imf calculated from data obtained by the WPL cloud radar during FIRE II. Other quantities such as median particle diameter, concentration, and ice mass content can also be estimated with this radar, and we expect to also examine normalized profiles of these quantities in time for the 1993 FIRE II meeting.

Orr, Brad W.↗
NASA NTRS2020

Evaluation of the MODIS Collection 6 Multilayer Cloud Detection Algorithm Through Comparisons with Cloudsat Cloud Profiling Radar and CALIPSO CALIOP Products

Since multilayer cloud scenes are common in the atmosphere and can be an important source of uncertainty in passive satellite sensor cloud retrievals, the MODIS MOD06 and MYD06 standard cloud optical property products include a multilayer cloud detection algorithm to assist with data quality assessment. This paper presents an evaluation of the Aqua MODIS MYD06 Collection 6 multilayer cloud detection algorithm through comparisons with active Cloud Profiling Radar (CPR) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) products that have the ability to provide cloud vertical distributions and directly classify multilayer cloud scenes and layer properties. To compare active sensor products with an imager such as MODIS, it is first necessary to define multilayer clouds in the context of their radiative impact on cloud retrievals. Three main parameters have thus been considered in this evaluation: (1) the maximum separation distance between two cloud layers, (2) the thermodynamic phase of those layers and (3) the upper-layer cloud optical thickness. The impact of including the Pavolonis–Heidinger multilayer cloud detection algorithm, introduced in Collection 6, to assist with multilayer cloud detection has also been assessed. For the year 2008, the MYD06 C6 multilayer cloud detection algorithm identifies roughly 20% of all cloudy pixels as multilayer (decreasing to about 13% if the Pavolonis–Heidinger algorithm output is not used). Evaluation against the merged CPR and CALIOP2B-CLDCLASS-lidar product shows that the MODIS multilayer detection results are quite sensitive to how multilayer clouds are defined in the radar and lidar product and that the algorithm performs better when the optical thickness of the upper cloud layer is greater than about 1.2 with a minimum layer separation distance of 1 km. Finally, we find that filtering the MYD06 cloud optical properties retrievals using the multilayer cloud flag improves aggregated statistics, particularly for ice cloud effective radius.

MODIS↗
NASA NTRS2015

Cloud-Radiative Driving of the Madden-Julian Oscillation as Seen by the A-Train

Cloud and water vapor radiative heating anomalies associated with convection may be an effective source of moist static energy driving the Madden-Julian Oscillation (MJO). In this paper five years of radiative heating profiles derived from CloudSat radar and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation data are analyzed to document radiative heating anomalies during the MJO. Atmospheric shortwave absorption and surface longwave radiation anomalies are of opposite sign and 10-20% as large as top-of-atmosphere outgoing longwave radiation (OLR) anomalies, confirming that OLR provides a useful estimate of the total column radiative heating anomaly. Positive anomalies generally peak about one week before the MJO peak and are smallest over the Indian Ocean. Anomalies over the Maritime Continent are strongest, and coincident with the MJO peak. Shortwave heating profile anomalies are about half as large as longwave anomalies in the active region of the MJO but generally of opposite sign; thus shortwave heating damps the longwave destabilization of the lower troposphere. The exception is the onset phase of the MJO, where shortwave and longwave heating anomalies due to thin cirrus are both positive in the upper troposphere and exert a stabilizing influence. Specific humidity anomalies in the middle troposphere reach 0.5 g kg(exp. -1), but the associated clear sky heating anomaly is very small. Radiative enhancement of column moist static energy becomes significant about 10 days before the MJO peak, when precipitation anomalies are still increasing, and then remains high after the MJO peak after precipitation has begun to decline.

symbols↗
NASA NTRSDate not supplied

A Neural Network Parametrization of Volumetric Cloud Fraction Profiles Using Satellite Observations and MERRA-2 Reanalysis Meteorological Data

Clouds play a crucial role in regulating the hydrologic cycle and Earth's radiative energy budget, yet they are often poorly represented in global climate models (GCMs). This study applies deep machine learning techniques to develop a physical parameterization of volumetric cloud fraction (VCF), the fraction of a 3-D grid volume occupied by clouds using satellite lidar-radar measurements. The neural network (NN) captures the complicated relationships between observed VCF profiles and collocated meteorological variables from MERRA-2 reanalysis data. Our results show that the NN model, particularly a sequence-to-sequence long short-term memory (LSTM) network with a sixfactor loss function, effectively learns the underlying cloud physical processes. The NN model outperforms MERRA-2 reanalysis in representing low-level clouds in tropical and subtropical regions and low- and middle-level clouds over midlatitude storm-track regions, and also improves VCF histograms. These improvements are reflected in the vertical distributions of zonally, meridionally, and globally averaged VCFs, geographic distributions of low-, middle-, and high-level clouds, and seasonal variations in monthly-mean VCF. Furthermore, the NN predictions effectively capture the El Niño-Southern Oscillation (ENSO) effects and other interannual variations. The NN parameterization is further evaluated through a sensitivity analysis, in which a single predictor is perturbed at a time. This reveals that relative humidity (RH) is the dominant factor influencing variations in globally averaged VCF at low and middle altitudes, followed by temperature. At higher altitudes, temperature becomes the primary driver of VCF through its effect on RH. Changes in wind components had minimal impact on globally averaged VCF.

Shan Zeng↗
NASA NTRS2021

Biases in CloudSat Falling Snow Estimates Resulting from Daylight-Only Operations

Falling snow is a key component of the Earth’s water cycle, and space-based observations provide the best current capability to evaluate it globally. The Cloud Profiling Radar (CPR) on board CloudSat is sensitive to snowfall, and other satellite missions and climatological models have used snowfall properties measured by it for evaluating and comparing against their snowfall products. Since a battery anomaly in 2011, the CPR has operated in a Daylight-Only Operations (DO-Op) mode, in which it makes measurements primarily during only the daylit portion of its orbit. This work provides estimates of biases inherent in global snowfall amounts derived from CPR measurements due to this shift to DO-Op mode. We use CloudSat’s snowfall measurements during its Full Operations (Full-Op) period prior to the battery anomaly to evaluate the impact of the DO-Op mode sampling. For multi-year global mean values, the snowfall fraction during DO-Op changes by −10.16% and the mean snowfall rate changes by −8.21% compared with Full-Op. These changes are driven by the changes in sampling in DO-Op and are very little influenced by changes in meteorology between the Full-Op and DO-Op periods. The results highlight the need to sample consistently with the CloudSat observations or to adjust snowfall estimates derived from CloudSat when using DO-Op data to evaluate other precipitation products.

Lisa Milani↗
DOE OSTI2025

Orbital-Radar v1.0.0: a tool to transform suborbital radar observations to synthetic EarthCARE cloud radar data

The Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) satellite developed by the European Space Agency (ESA) and the Japan Aerospace Exploration Agency (JAXA) launched in May 2024 carries a novel 94 GHz cloud profiling radar (CPR) with Doppler capability. This work describes the open-source instrument simulator Orbital-Radar, which transforms high-resolution radar data from field observations or forward simulations of numerical models to CPR primary measurements and uncertainties. The transformation accounts for sampling geometry and surface effects. We demonstrate Orbital-Radar's ability to provide realistic CPR views of typical cloud and precipitation scenes. The presented case studies show small-scale convection, marine stratus clouds, and Arctic mixed-phase cloud cases. These results provide valuable insights into the capabilities and challenges of the EarthCARE CPR mission and its advantages over the CloudSat CPR. Finally, Orbital-Radar allows for evaluating kilometre-scale numerical weather prediction models with EarthCARE CPR observations. So, Orbital-Radar can generate calibration and validation (Cal/Val) data sets already pre-launch. Nevertheless, an evaluation of synthetic CPR output data to accurate EarthCARE CPR data is missing.

54 ENVIRONMENTAL SCIENCES↗