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NASA NTRS2017

Information Theoretic Evaluation of Satellite Soil Moisture Retrievals

Microwave radiometry has a long legacy of providing estimates of remotely sensed near surfacesoil moisture measurements over continental and global scales. A consistent assessment of theerrors and uncertainties associated with these retrievals is important for their effective utilization in modeling, data assimilation and end-use application environments. This article presents an evaluationof soil moisture retrieval products from AMSR-E, ASCAT, SMOS, AMSR2 and SMAPinstruments using information theory-based metrics. These metrics rely on time series analysis ofsoil moisture retrievals for estimating the measurement error, level of randomness (entropy) andregularity (complexity) of the data. The results of the study indicate that the measurement errors inthe remote sensing retrievals are significantly larger than that of the ground soil moisture measurements.The SMAP retrievals, on the other hand, were found to have reduced errors (comparable to Preprint submitted to Remote Sensing of Environment October 1, 2017those of in-situ datasets), particularly over areas with moderate vegetation. The SMAP retrievals also demonstrate high information content relative to other retrieval products, with higher levelsof complexity and reduced entropy. Finally, a joint evaluation of the entropy and complexity ofremotely sensed soil moisture products indicates that the information content of the AMSR-E, ASCAT,SMOS and AMSR2 retrievals is low, whereas SMAP retrievals show better performance. The use of information theoretic assessments is effective in quantifying the required levels of improvements needed in the remote sensing soil moisture retrievals to enhance their utility and information content.

Information theory↗
NASA NTRS2019

Characteristics of Total Column CO2 Retrievals from the OCO Missions: Biases, Information Content and Implications for Flux Inversions

The Orbiting Carbon Observatory-2 and Orbiting Carbon Observatory-3, launched in 2015 and 2019, respectively, are intended to collect and deliver high-resolution observations of CO2 with unprecedented space and time coverage. Observations of CO2 from these remote-sensing missions (also known as XCO2, or column-based average, dry air mole fraction of CO2) are then used by the global carbon cycle community to answer a wide range of science questions, from the distribution and quantification of global and regional CO2 source-sink patterns to quantification of anthropogenic sources at urban scales. Even though we have had the OCO-2 mission flying for a few years now, the retrieval algorithms are continuously evolving and improving to deliver XCO2 retrievals with very high precision and high accuracy (or low biases). In this presentation, we will discuss a simple yet effective quantitative framework that has been developed by the OCO-2 flux team to evaluate the information content of these XCO2 retrievals as soon as they are released, i.e., with lower latency than full-scale flux inversions. This framework serves as a precursor to advanced inverse modeling frameworks and is intended to provide an early but accurate assessment of the signal present in the satellite retrievals, the robustness of that signal, and the ability of these retrievals to resolve patterns in CO2 surface fluxes that cannot be resolved by our current network of surface sites. Specific results will tackle a tiered set of questions that are being addressed using this framework: (a) what are the distribution of retrievals in the different modes of operation and how do they vary in space and time? (b) what is the information that is being given to the inverse modeling frameworks from the space-based data, information above and beyond what is provided by the in-situ data? and (c) how do these factors influence our choices for doing flux inversions with the satellite retrievals? While the primary focus of the results will be on application of this technique to mature OCO-2 retrievals, we will show early results for a couple of months of OCO-3 retrievals. For the time-period that the retrievals from the two missions overlap, we will highlight how this framework allows us to effortlessly put the information from OCO-3 and OCO-2 on an equal footing, thus enabling easy comparison between the two pioneering missions.

Chatterjee, Abhishek↗
NASA NTRS2021

The Thermal Infrared Optical Depth of Mineral Dust Retrieved from Integrated CALIOP and IIR Observations

Recent studies reveal that a higher fraction of coarse mineral dust particles than that estimated by climate model simulations has been observed in the atmosphere, leading to a more significant positive (i.e., warming) longwave (LW) thermal infrared (TIR) direct radiative effect (DRE). However, the magnitude of this DRE remains highly uncertain because our understanding of the radiative properties, quantitatively represented by the optical depth of dust, especially information on the TIR, remains limited. This study presents a simple approach to retrieve the thermal infrared dust aerosol optical depth (DAODTIR) over oceans during nighttime using the observations from the Infrared Imaging Radiometer (IIR) and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard CALIPSO. For each cloud-free dust-laden profile identified by the IIR-CALIOP observation, a Lookup-Table (LUT) of the 10.6 μm IIR band brightness temperatures difference (dBT) under different DAODTIR with respect to their dust-free BTs is constructed based on the CALIOP retrieved dust vertical profile and pre-assumed dust scattering properties using a fast radiative transfer model. Then the DAODTIR is retrieved by projecting the IIR-observed dBT on the LUT. Sensitivity studies show that the DAODTIR retrieval at 10.6 μm is more susceptible to the dust particle size distribution (PSD) assumption than dust refractive indices. To estimate the uncertainty caused by PSD assumption, two DAODTIR retrieval products, one based on the dust PSD from the AERONET at Cape Verde and the other on an in situ measured PSD from the recent Fennec campaign, are provided. The retrieval uncertainty is mainly contributed by the BT difference between the observation and simulation using auxiliary atmospheric data. The climatology of the retrieval from 2013 to 2019 shows confident spatiotemporal variations of DAODTIR with the global-averaged value of 0.006 and 0.008 based on different pre-assumed dust PSDs. Climatological results agree reasonably well with two independent DAODTIR retrieval products based on the Infrared Atmospheric Sounding Interferometer (IASI) over the active dust transport regions, such as North and Tropical Atlantic (r = 0.904 and 0.819) and Indian Ocean (r = 0.832). The seasonal and interannual variation is also well-compared (r = 0.758) with AERONET coarse-mode AOD at 97 selected sites. The synergic CALIOP observation allows the retrieved DAODTIR to directly compare with the extrapolated DAODTIR from DAOD in visible (i.e., 532 nm), which helps evaluate the observational constraints on DAODTIR. This study offers a unique prospect of collocating active lidar and passive IR observations for retrieving dust DAODTIR.

aerosol↗
NASA NTRS2022

Simultaneous Retrievals of Biomass Burning Aerosols and Trace Gases From the Ultraviolet to Near-Infrared Over Northern Thailand During the 2019 Pre-Monsoon Season

With the advent of spaceborne spectroradiometers in a geostationary constellation, measuring high spectral resolution ultraviolet–visible (UV-VIS) and selected near-/shortwave-infrared (NIR/SWIR) radiances can enable the probing of the life cycle of key atmospheric trace gases and aerosols at higher temporal resolutions over the globe. The UV-VIS measurements are important for retrieving several key trace gases (e.g., O 3 , SO 2 , NO 2 , and HCHO) and particularly for deriving aerosol characteristics (e.g., aerosol absorption and vertical profile). This study examines the merit of simultaneous retrievals of trace gases and aerosols using a ground-based spectroradiometer covering the UV-NIR to monitor their physicochemical processes and to obtain reliable aerosol information for various applications. During the 2019 pre-monsoon season over northern Thailand, we deployed a ground-based SMART–s (Spectral Measurements for Atmospheric Radiative Transfer–spectroradiometer) instrument, which is an extended-range Pandora with reliable radiometric calibration in the 330–820 nm range, to retrieve remotely sensed chemical and aerosol properties for the first time near biomass burning sources. The high spectral resolution (∼ 1.0 nm full width half maximum with ∼ 3.7 × oversampling) of sun and sky measurements from SMART–s provides several key trace gases (e.g., O 3 , NO 2 , and H 2 O) and aerosol properties covering the UV where significant light absorption occurs by the carbonaceous particles. During the measurement period, highly correlated total column amounts of NO 2 and aerosol optical thickness (т aer ) retrieved from SMART–s (correlation coefficient, R=0.74) indicated their common emissions from biomass burning events. The SMART–s retrievals of the spectral single scattering albedo (ω 0 ) of smoke aerosols showed an abrupt decrease in the UV, which is an important parameter dictating photochemical processes in the atmosphere. The values of ω 0 and column precipitable water vapor (H 2 O) gradually increase with the mixing of biomass burning smoke particles and higher water vapor concentrations when approaching the monsoon season. The retrieved ω 0 and weighted mean radius of fine-mode aerosols from SMART–s showed positive correlations with the H 2 O (R=0.81 for ω 0 at 330 nm and 0.56 for the volume-weighted mean radius), whereas the real part of the refractive index of fine-mode aerosol (n f ) showed negative correlations (R = -0.61 at 330 nm), which suggest that aerosol aging processes including hygroscopic growth (e.g., humidification and cloud processing) can be a major factor affecting the temporal trends of aerosol optical properties. Retrieved n f and ω 0 were closer to those of the water droplet (i.e., n f of about 1.33 and ω 0 of about 1.0) under lower amounts of NO 2 during the measurement period; considering that the NO 2 amounts in the smoke may indicate the aging of the plume after emission due to its short lifetime, the tendency is also consistent with active hygroscopic processes of the aerosols over this area. Retrieved UV aerosol properties from SMART–s generally support the assumed smoke aerosol models (i.e., the spectral shape of aerosol absorption) used in NASA's current satellite algorithms, and their spectral ω 0 retrievals from ground and satellites showed good agreements (R = 0.73–0.79). However, temporal and spectral variabilities in the aerosol absorption properties in the UV emphasize the importance of a realistic optical model of aerosols for further improvements in satellite retrievals.

biomass burning↗
DOE OSTI2020

Retrieval of Transuranic Drums with Deflagration to Detonation Potential from a Vault at the Oak Ridge National Laboratory - 20207

During the final phase of a long-term project, a total of 80 TRU waste drums at ORNL have been retrieved from a below-grade storage vault in which they have remained untouched for about 25 years. The drums, which house material generated from past ORNL operations, are now stored and ready for eventual transfer to the Transuranic Waste Processing Center, located on Highway 95 in Oak Ridge. The Transuranic Waste Processing Center will process and ship the drums to WIPP in Carlsbad, New Mexico, for disposal. Workers lifted the drums one-by-one from the underground storage vault with a 110-ton crane over a one-week period in October 2017. Although the physical work was performed in one week, years of detailed planning preceded the fieldwork. A key obstacle needed to be overcome before the project could safely proceed. This obstacle involved the need to ensure controls were developed for the Deflagration to Detonation Transition (DDT) potential. DDT refers to a phenomenon in ignitable mixtures of a flammable gas and air (or oxygen) when a sudden transition takes place from a deflagration type of combustion to a detonation type of explosion. Basically, these drums, under certain circumstances, have a potential to detonate and overpressurize. Extensive analysis of the drums verified that detonation, while unlikely, was possible. Of the total 103 drums that were retrieved or moved in preparation for retrieval, 18 had DDT potential. Extensive security requirements were also necessary, due to the nature of the material, and this added another layer of complexity to the fieldwork. Prior to retrieving the drums from the underground storage vault, space needed to be made for them within the various above-grade facilities. Inventory and security requirements dictated what could be stored where, and the first step of the field work involved a number of drum movements in preparation for storage of the newly retrieved drums. This effort involved moving 21 drums already in the storage area to facilitate the addition of the newly retrieved ones. The retrieval project required a total of 107 critical lifts using the 110-ton crane, under 3 critical lift plans. The TRU waste storage facility is a Category 2 Nuclear Facility and is managed under an extensive Documented Safety Analysis. The TRU retrieval project was conducted in accordance with DOE Order 425.1D and included three Implementation Verification Reviews, two Management Assessments, one Readiness Assessment, and seven mock-ups in preparation for the field work. Despite being stored for 25 years, the drums were in good shape due to the integrity of the facilities where the drums were stored. Of the 80 drums that were retrieved, 12 were placed in special containers called 'overpacks' based on their content. The drums were removed safely and without incident, thanks to a lot of preparation and the efforts of a highly skilled team. Lessons learned through the DDT control development and the drum retrieval and overpacking process could be beneficial to others having to manage similar drums of TRU waste. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Evaluation and Applications of Multi-Instrument Boundary-Layer Thermodynamic Retrievals

Recent reports have highlighted the need for improved observations of the atmosphere boundary layer. In this study, we explore the combination of ground-based active and passive remote sensors deployed for thermodynamic profiling to analyze various boundary-layer observation strategies. Optimal-estimation retrievals of thermodynamic profiles from Atmospheric Emitted Radiance Interferometer (AERI) observed spectral radiance are compared with and without the addition of active sensor observations from a May–June 2017 observation period at the Atmospheric Radiation Measurement Southern Great Plains site. In all, three separate thermodynamic retrievals are considered here: retrievals including AERI data only, retrievals including AERI data and Vaisala water vapour differential-absorption lidar data, and retrievals including AERI data and Raman lidar data. First, the three retrievals are compared to each other and to reference radiosonde data over the full observation period to obtain a bulk understanding of their differences and characterize the impact of clouds on these retrieved profiles. These analyses show that the most significant differences are in the water vapour field, where the active sensors are better able to represent the moisture gradient in the entrainment zone near the boundary-layer top. Furthermore, we also explore how differences in retrievals may affect results of applied analyses including land–atmosphere coupling, convection indices, and severe storm environmental characterization. Overall, adding active sensors to the optimal-estimation retrieval shows some added information, particularly in the moisture field. Given the costs of such platforms, the value of that added information must be weighed for the application at hand.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Systematic Assessment of Retrieval Methods for Canopy Far-Red Solar-Induced Chlorophyll Fluorescence Using High-Frequency Automated Field Spectroscopy

Remote sensing of solar-induced chlorophyll fluorescence (SIF) offers potential to infer photosynthesis across scales and biomes. Many retrieval methods have been developed to estimate top-of-canopy SIF using ground-based spectroscopy. However, inconsistencies among methods may confound interpretation of SIF dynamics, eco-physiological/environmental drivers, and its relationship with photosynthesis. Using high temporal- and spectral resolution ground-based spectroscopy, we aimed to (1) evaluate performance of SIF retrieval methods under diverse sky conditions using continuous field measurements; (2) assess method sensitivity to fluctuating light, reflectance, and fluorescence emission spectra; and (3) inform users for optimal ground-based SIF retrieval. Analysis included field measurements from bi-hemispherical and hemispherical-conical systems and synthetic upwelling radiance constructed from measured downwelling radiance, simulated reflectance, and simulated fluorescence for benchmarking. Fraunhofer-based differential optical absorption spectroscopy (DOAS) and singular vector decomposition (SVD) retrievals exhibit convergent SIF-PAR relationships and diurnal consistency across different sky conditions, while O2A-based spectral fitting method (SFM), SVD, and modified Fraunhofer line discrimination (3FLD) exhibit divergent SIF-PAR relationships across sky conditions. We find that such behavior holds across system configurations, though hemispherical-conical systems diverge less across sky conditions. O 2 A retrieval accuracy, influenced by atmospheric distortion, improves with a narrower fitting window and when training SVD with temporally local spectra. This may impact SIF-photosynthesis relationships interpreted by previous studies using O 2 A-based retrievals with standard (759–767.76 nm) fitting windows. Fraunhofer-based retrievals resist atmospheric impacts but are noisier and more sensitive to assumed SIF spectral shape than O 2 A-based retrievals. We recommend SVD or SFM using reduced fitting window (759.5–761.5 nm) for robust far-red SIF retrievals across sky conditions.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2013

Results from CrIS-ATMS Obtained Using the AIRS Science Team Retrieval Methodology

AIRS was launched on EOS Aqua in May 2002, together with AMSU-A and HSB (which subsequently failed early in the mission), to form a next generation polar orbiting infrared and microwave atmospheric sounding system. AIRS/AMSU had two primary objectives. The first objective was to provide real-time data products available for use by the operational Numerical Weather Prediction Centers in a data assimilation mode to improve the skill of their subsequent forecasts. The second objective was to provide accurate unbiased sounding products with good spatial coverage that are used to generate stable multi-year climate data sets to study the earth's interannual variability, climate processes, and possibly long-term trends. AIRS/AMSU data for all time periods are now being processed using the state of the art AIRS Science Team Version-6 retrieval methodology. The Suomi-NPP mission was launched in October 2011 as part of a sequence of Low Earth Orbiting satellite missions under the "Joint Polar Satellite System" (JPSS). NPP carries CrIS and ATMS, which are advanced infra-red and microwave atmospheric sounders that were designed as follow-ons to the AIRS and AMSU instruments. The main objective of this work is to assess whether CrIS/ATMS will be an adequate replacement for AIRS/AMSU from the perspective of the generation of accurate and consistent long term climate data records, or if improved instruments should be developed for future flight. It is critical for CrIS/ATMS to be processed using an algorithm similar to, or at least comparable to, AIRS Version-6 before such an assessment can be made. We have been conducting research to optimize products derived from CrIS/ATMS observations using a scientific approach analogous to the AIRS Version-6 retrieval algorithm. Our latest research uses Version-5.70 of the CrIS/ATMS retrieval algorithm, which is otherwise analogous to AIRS Version-6, but does not yet contain the benefit of use of a Neural-Net first guess start-up system which significantly improved results of AIRS Version-6. Version-5.70 CrIS/ATMS temperature profile and surface skin temperature retrievals are of very good quality, and are better than AIRS Version-5 retrievals, but are still significantly poorer than those of AIRS Version-6. CrIS/ATMS retrievals should improve when a Neural-Net start-up system is ready for use. We also examined CrIS/ATMS retrievals generated by NOAA using their NUCAPS retrieval algorithm, which is based on earlier versions of the AIRS Science Team retrieval algorithms. We show that the NUCAPS algorithm as currently configured is not well suited for climate monitoring purposes.

CrIS↗
NASA NTRS2015

Global Precipitation Measurement (GPM) Mission: Precipitation Processing System (PPS) GPM Mission Gridded Text Products Provide Surface Precipitation Retrievals

In February 2015, the Global Precipitation Measurement (GPM) mission core satellite will complete its first year in space. The core satellite carries a conically scanning microwave imager called the GPM Microwave Imager (GMI), which also has 166 GHz and 183 GHz frequency channels. The GPM core satellite also carries a dual frequency radar (DPR) which operates at Ku frequency, similar to the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar, and a new Ka frequency. The precipitation processing system (PPS) is producing swath-based instantaneous precipitation retrievals from GMI, both radars including a dual-frequency product, and a combined GMIDPR precipitation retrieval. These level 2 products are written in the HDF5 format and have many additional parameters beyond surface precipitation that are organized into appropriate groups. While these retrieval algorithms were developed prior to launch and are not optimal, these algorithms are producing very creditable retrievals. It is appropriate for a wide group of users to have access to the GPM retrievals. However, for researchers requiring only surface precipitation, these L2 swath products can appear to be very intimidating and they certainly do contain many more variables than the average researcher needs. Some researchers desire only surface retrievals stored in a simple easily accessible format. In response, PPS has begun to produce gridded text based products that contain just the most widely used variables for each instrument (surface rainfall rate, fraction liquid, fraction convective) in a single line for each grid box that contains one or more observations.This paper will describe the gridded data products that are being produced and provide an overview of their content. Currently two types of gridded products are being produced: (1) surface precipitation retrievals from the core satellite instruments GMI, DPR, and combined GMIDPR (2) surface precipitation retrievals for the partner constellation satellites. Both of these gridded products are generated for a.25 degree x.25 degree hourly grid, which are packaged into daily ASCII (American Standard Code for Information Interchange) files that can downloaded from the PPS FTP (File Transfer Protocol) site. To reduce the download size, the files are compressed using the gzip utility.This paper will focus on presenting high-level details about the gridded text product being generated from the instruments on the GPM core satellite. But summary information will also be presented about the partner radiometer gridded product. All retrievals for the partner radiometer are done using the GPROF2014 algorithmusing as input the PPS generated inter-calibrated 1C product for the radiometer.

precipitation↗
NASA NTRS2021

Evaluation and Applications of Multi-Instrument Boundary-Layer Thermodynamic Retrievals

Recent reports have highlighted the need for improved observations of the boundary layer. In this study, we explore the combination of ground-based active and passive remote sensors deployed for thermodynamic profiling to analyze various boundary-layer observation strategies. Optimal-estimation retrievals of thermodynamic profiles from Atmospheric Emitted Radiance Interferometer (AERI) observed spectral radiance are compared with and without the addition of active sensor observations from a May–June 2017 observation period at the Atmospheric Radiation Measurement–Southern Great Plains Site. In all, three separate thermodynamic retrievals are considered here: retrievals including AERI data only, retrievals including AERI data and Vaisala water vapour differential absorption lidar data, and retrievals including AERI data and Raman lidar data. First, the three retrievals are compared to each other and to reference radiosonde data over the full observation period to get a bulk understanding of their differences and characterize the impact of clouds on these retrieved profiles. These analyses show that the most significant differences are in the water vapour field, where the active sensors are better able to represent the moisture gradient in the entrainment zone near boundary layer top. We also explore how differences in retrievals may impact results of applied analyses including land–atmosphere coupling, convection indices, and severe storm environmental characterization. Overall, adding active sensors to the optimal-estimation retrieval showed some added information, particularly in the moisture field. Given the costs of such platforms, the value of that added information must be weighed for the application at hand.

boundary-layer observation↗
DOE OSTI2023

Evaluation of liquid cloud albedo susceptibility in E3SM using coupled eastern North Atlantic surface and satellite retrievals

The impact of aerosol number concentration on cloud albedo is a persistent source of spread in global climate predictions due to multi-scale, interactive atmospheric processes that remain difficult to quantify. We use 5 years of geostationary satellite and surface retrievals at the US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) eastern North Atlantic (ENA) site in the Azores to evaluate the representation of liquid cloud albedo susceptibility for overcast cloud scenes in the DOE Energy Exascale Earth System Model version 1 (E3SMv1) and provide possible reasons for model–observation discrepancies. The overall distribution of surface 0.2 % CCN concentration values is reasonably simulated, but simulated liquid water path (LWP) is lower than observed and layer mean droplet concentration (N d ) comparisons are highly variable depending on the N d retrieval technique. E3SMv1's cloud albedo is greater than observed for given LWP and N d values due to a lower cloud effective radius than observed. However, the simulated albedo response to N d is suppressed due to a correlation between the solar zenith angle (SZA) and N d created by the seasonal cycle that is not observed. Controlling for this effect by examining the cloud optical depth (COD) shows that E3SMv1's COD response to CCN concentration is greater than observed. For surface-based retrievals, this is only true after controlling for cloud adiabaticity because E3SMv1's adiabaticities are much lower than observed. Assuming a constant adiabaticity in surface retrievals as done in top-of-atmosphere (TOA) retrievals narrows the retrieved ln N d distribution, which increases the cloud albedo sensitivity to ln N d to match the TOA sensitivity. The greater sensitivity of COD to CCN is caused by a greater Twomey effect in which the sensitivity of N d to CCN is greater than observed for TOA-retrieved N d , and once model–observation cloud adiabaticity differences are removed, this is also true for surface-retrieved N d . The LWP response to N d in E3SMv1 is overall negative as observed. Despite reproducing the observed LWP–N d relationship, observed clouds become much more adiabatic as N d increases, while E3SMv1 clouds do not, associated with more heavily precipitating clouds that are partially but not completely caused by deeper clouds and weaker inversions in E3SMv1. These cloud property differences indicate that the negative LWP–N d relationship is likely not caused by the same mechanisms in E3SMv1 and observations. The negative simulated LWP response also fails to mute the excessively strong Twomey effect, highlighting potentially important confounding factor effects that likely render the LWP–N d relationship non-causal. N d retrieval scales and assumptions, particularly related to cloud adiabaticity, contribute to substantial spreads in the model–observation comparisons, though enough consistency exists to suggest that aerosol activation, drizzle, and entrainment processes are critical areas to focus E3SMv1 development for improving the fidelity of aerosol–cloud interactions in E3SM.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1996

SSM/I Rain Retrievals Within a Unified All-Weather Ocean Algorithm

A new method for the physical retrieval of rain rates from satellite microwave radiometers is presented and compared to two other rainfall climatologies derived from satellites. The method is part of a unified ocean parameter retrieval algorithm that is based on the fundamental principles of radiative transfer. The algorithm simultaneously finds near-surface wind speed W, columnar water vapor V, columnar cloud liquid water L, rain rate R, and effective radiating temperature T(sub U) for the upwelling radiation. The performance of the algorithm in the absence of rain is discussed in Wentz, and this paper focuses on the rain component of the algorithm. A particular strength of the unified algorithm is its ability to 'orthogonalize' the retrievals so that there is minimum cross-talk between the retrieved parameters. For example, comparisons of the retrieved water vapor with radiosonde observations show that there is very little correlation between the water vapor retrieval error and rain rate. For rain rates from 1 to 15 mm/h, the rms difference between the retrieved water vapor and the radiosonde value is 5 mm. A novel feature of the rain retrieval method is a beamfilling correction that is based upon the ratio of the retrieved liquid water absorption coefficients at 37 GHz and 19.35 GHz. This ratio decreases by about 40% when heavy and light rain co-exist within the SSM/I footprint as compared to the case of uniform rain. This correction has the effect of increasing the rain rate when the spectral ratio of the absorption coefficients is small. Even with this beamfilling correction, tropical rainfall is still unrealistically low when the freezing level in the tropics (approx. 5 km) is used to specify the rain layer thickness. We restore realism by reducing the assumed averaged tropical rain layer thickness to 3 km, thereby accounting for the existence of warm rain processes in which the rain layer does not extend to the freezing level. Global rain rates are produced for the 1991 through 1994 period from observations taken by microwave radiometers (SSM/I) that are aboard two polar-orbiting satellites. We find that approximately 6% of the SSM/I observations detect measurable rain rates (R greater than 0.2 mm/h). Zonal averages of the rain rates show the peak at the intertropical convergence zone (ITCZ) is quite narrow in meridional extent and varies from about 7 mm/day in the winter to a maximum 11 mm/day in the summer. Very low precipitation rates (less than 0.3 mm/day) are observed in those areas of subsidence influenced by the large semipermanent anticyclones. In general, these features are similar to those reported in previously published rain climatologies. However, significant differences do exists between our rain rates and those produced by Spencer. These differences seem to be related to non-precipitating cloud water.

Wentz, Frank J.↗
NASA NTRS1997

Correction of Rayleigh Scattering Effects in Cloud Optical Thickness Retrievals

We present results that demonstrate the effects of Rayleigh scattering on the 9 retrieval of cloud optical thickness at a visible wavelength (0.66 Am). The sensor-measured radiance at a visible wavelength (0.66 Am) is usually used to infer remotely the cloud optical thickness from aircraft or satellite instruments. For example, we find that without removing Rayleigh scattering effects, errors in the retrieved cloud optical thickness for a thin water cloud layer (T = 2.0) range from 15 to 60%, depending on solar zenith angle and viewing geometry. For an optically thick cloud (T = 10), on the other hand, errors can range from 10 to 60% for large solar zenith angles (0-60 deg) because of enhanced Rayleigh scattering. It is therefore particularly important to correct for Rayleigh scattering contributions to the reflected signal from a cloud layer both (1) for the case of thin clouds and (2) for large solar zenith angles and all clouds. On the basis of the single scattering approximation, we propose an iterative method for effectively removing Rayleigh scattering contributions from the measured radiance signal in cloud optical thickness retrievals. The proposed correction algorithm works very well and can easily be incorporated into any cloud retrieval algorithm. The Rayleigh correction method is applicable to cloud at any pressure, providing that the cloud top pressure is known to within +/- 100 bPa. With the Rayleigh correction the errors in retrieved cloud optical thickness are usually reduced to within 3%. In cases of both thin cloud layers and thick ,clouds with large solar zenith angles, the errors are usually reduced by a factor of about 2 to over 10. The Rayleigh correction algorithm has been tested with simulations for realistic cloud optical and microphysical properties with different solar and viewing geometries. We apply the Rayleigh correction algorithm to the cloud optical thickness retrievals from experimental data obtained during the Atlantic Stratocumulus Transition Experiment (ASTEX) conducted near the Azores in June 1992 and compare these results to corresponding retrievals obtained using 0.88 Am. These results provide an example of the Rayleigh scattering effects on thin clouds and further test the Rayleigh correction scheme. Using a nonabsorbing near-infrared wavelength lambda (0.88 Am) in retrieving cloud optical thickness is only applicable over oceans, however, since most land surfaces are highly reflective at 0.88 Am. Hence successful global retrievals of cloud optical thickness should remove Rayleigh scattering effects when using reflectance measurements at 0.66 Am.

Wang, Meng-Hua↗
NASA NTRS2006

Space-Based Near-Infrared CO2 Measurements: Testing the Orbiting Carbon Observatory Retrieval Algorithm and Validation Concept Using SCIAMACHY Observations over Park Falls, Wisconsin

Space-based measurements of reflected sunlight in the near-infrared (NIR) region promise to yield accurate and precise observations of the global distribution of atmospheric CO2. The Orbiting Carbon Observatory (OCO) is a future NASA mission, which will use this technique to measure the column-averaged dry air mole fraction of CO2 (XCO2) with the precision and accuracy needed to quantify CO2 sources and sinks on regional scales (approx.1000 x 1000 sq km and to characterize their variability on seasonal timescales. Here, we have used the OCO retrieval algorithm to retrieve XCO2 and surface pressure from space-based Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) measurements and from coincident ground-based Fourier transform spectrometer (FTS) measurements of the O2 A band at 0.76 mm and the 1.58 mm CO2 band for Park Falls,Wisconsin. Even after accounting for a systematic error in our representation of the O2 absorption cross sections, we still obtained a positive bias between SCIAMACHY and FTS XCO2 retrievals of approx.3.5%. Additionally, the retrieved surface pressures from SCIAMACHY systematically underestimate measurements of a calibrated pressure sensor at the FTS site. These findings lead us to speculate about inadequacies in the forward model of our retrieval algorithm. By assuming a 1% intensity offset in the O2 A band region for the SCIAMACHY XCO2 retrieval, we significantly improved the spectral fit and achieved better consistency between SCIAMACHY and FTS XCO2 retrievals. We compared the seasonal cycle of XCO2 at Park Falls from SCIAMACHY and FTS retrievals with calculations of the Model of Atmospheric Transport and Chemistry/Carnegie-Ames-Stanford Approach (MATCH/CASA) and found a good qualitative agreement but with MATCH/CASA underestimating the measured seasonal amplitude. Furthermore, since SCIAMACHY observations are similar in viewing geometry and spectral range to those of OCO, this study represents an important test of the OCO retrieval algorithm and validation concept using NIR spectra measured from space. Finally, we argue that significant improvements in precision and accuracy could be obtained from a dedicated CO2 instrument such as OCO, which has much higher spectral and spatial resolutions than SCIAMACHY. These measurements would then provide critical data for improving our understanding of the carbon cycle and carbon sources and sinks.

carbon dioxide↗
NASA NTRS2007

A Slow Retrieval Algorithm for Satellite and Surface Based Instruments

We present results of a retrieval algorithm for satellite and ground based instruments using the Arizona radiative transfer code. A state vector describing the atmospheric and surface condition is iteratively modified until the calculated radiances match the observed values. Elements of the state vector include: aerosol concentrations, radius, optical properties, mass-weighted altitudes, chlorophyll concentration and wind speed. While computationally expensive, many assumptions used in other retrieval algorithms are not invoked. We present co-located retrievals for MODIS, SEAWIFS and nearby AERONET sites. MODIS AQUA and SEA WIFS: Ten MODIS (.412 - 2.110 microns) and eight SEA WIFS (.412-.865 microns) radiances (.412-.865 microns) include channels where aerosols absorb and reflect radiation. We focus on retrieving bio-mass burning aerosols that are advected over open ocean. Since chlorophyll absorbs at frequencies where black carbon absorbs, our retrieval algorithm accounts for chlorophyll absorption by simultaneously retrieving both aerosol and chlorophyll amount. Our retrieved chlorophyll concentrations are similar to those from the Ocean Color Group. AERONET: Both Almucantar and Principle plane radiances are used to retrieve the state of the atmosphere and ocean conditions. Our retrieved aerosol size distributions and optical properties are consistent with the aerosol inversions from the AERONET group.

Weaver, C.↗
NASA NTRS2008

Inter-comparison between AIRS and IASI through Retrieved Parameters

A State-of-the-art retrieval algorithm dealing with all-weather conditions has been applied to satellite/aircraft instruments retrieving cloud/surface and atmospheric conditions. High quality retrievals have been achieved from IASI data. Surface, cloud, and atmospheric structure and variation are well captured by IASI measurements and/or retrievals. The same retrieval algorithm is also applied to AIRS for retrieval inter-comparison. Both AIRS and IASI have a similar FOV size but AIRS has a higher horizontal resolution. AIRS data can be interpolated to IASI horizontal resolution for inter-comparison at the same geophysical locations, however a temporal variation between AIRS and IASI observations need to be considered. JAIVEx has employed aircraft to obtain the atmospheric variation filling the temporal gap between two satellites. First results show that both AIRS and IASI have a very similar vertical resolving power, atmospheric conditions are well captured by both instruments, and radiances are well calibrated. AIRS data shown in retrievals (e.g., surface emissivity and moisture) have a relatively higher noise level. Since the this type of retrieval is very sensitive to its radiance quality, retrieval products inter-comparison is an effective way to identify/compare their radiance quality, in terms of a combination of spectral resolution and noise level, and to assess instrument performance. Additional validation analyses are needed to provide more-definitive conclusions.

Zhou, Daniel K.↗
NASA NTRS2008

Retrievals of Thick Cloud Optical Depth from the Geoscience Laser Altimeter System (GLAS) by Calibration of Solar Background Signal

Laser beams emitted from the Geoscience Laser Altimeter System (GLAS), as well as other space-borne laser instruments, can only penetrate clouds to a limit of a few optical depths. As a result, only optical depths of thinner clouds (< about 3 for GLAS) are retrieved from the reflected lidar signal. This paper presents a comprehensive study of possible retrievals of optical depth of thick clouds using solar background light and treating GLAS as a solar radiometer. To do so we first calibrate the reflected solar radiation received by the photon-counting detectors of GLAS' 532 nm channel, which is the primary channel for atmospheric products. The solar background radiation is regarded as a noise to be subtracted in the retrieval process of the lidar products. However, once calibrated, it becomes a signal that can be used in studying the properties of optically thick clouds. In this paper, three calibration methods are presented: (I) calibration with coincident airborne and GLAS observations; (2) calibration with coincident Geostationary Operational Environmental Satellite (GOES) and GLAS observations of deep convective clouds; (3) calibration from the first principles using optical depth of thin water clouds over ocean retrieved by GLAS active remote sensing. Results from the three methods agree well with each other. Cloud optical depth (COD) is retrieved from the calibrated solar background signal using a one-channel retrieval. Comparison with COD retrieved from GOES during GLAS overpasses shows that the average difference between the two retrievals is 24%. As an example, the COD values retrieved from GLAS solar background are illustrated for a marine stratocumulus cloud field that is too thick to be penetrated by the GLAS laser. Based on this study, optical depths for thick clouds will be provided as a supplementary product to the existing operational GLAS cloud products in future GLAS data releases.

Yang, Yuekui↗
NASA NTRS2011

An Assessment of Differences Between Cloud Effective Particle Radius Retrievals for Marine Water Clouds from Three MODIS Spectral Bands

The Moderate Resolution Imaging Spectroradiometer (MODIS) cloud product provides three separate 1 km resolution retrievals of cloud particle effective radii (r (sub e)), derived from 1.6, 2.1 and 3.7 micron band observations. In this study, differences among the three size retrievals for maritime water clouds (designated as r (sub e), 1.6 r (sub e), 2.1 and r (sub e),3.7) were systematically investigated through a series of case studies and global analyses. Substantial differences are found between r (sub e),3.7 and r (sub e),2.1 retrievals (delta r (sub e),3.7-2.l), with a strong dependence on cloud regime. The differences are typically small, within +/- 2 micron, over relatively spatially homogeneous coastal stratocumulus cloud regions. However, for trade wind cumulus regimes, r (sub e),3.7 was found to be substantially smaller than r (sub e),2.1, sometimes by more than 10 micron. The correlation of delta r(sub e),3.7-2.1 with key cloud parameters, including the cloud optical thickness (tau), r (sub e) and a cloud horizontal heterogeneity index (H-sigma) derived from 250 m resolution MODIS 0.86 micron band observations, were investigated using one month of MODIS Terra data. It was found that differences among the three r (sub e) retrievals for optically thin clouds (tau <5) are highly variable, ranging from - 15 micron to 10 micron, likely due to the large MODIS retrieval uncertainties when the cloud is thin. The delta r (sub e),3.7-2.1 exhibited a threshold-like dependence on both r (sub e),2.l and H-sigma. The re,3.7 is found to agree reasonably well with re,2.! when re,2.l is smaller than about 15J-Lm, but becomes increasingly smaller than re,2.1 once re,2.! exceeds this size. All three re retrievals showed little dependence when H-sigma < 0.3 (defined as standard deviation divided by the mean for the 250 m pixels within a 1 km pixel retrieval). However, for H-=sigma >0.3, both r (sub e),1.6 and r (sub e),2.1 were seen to increase quickly with H-sigma. On the other hand, r (sub e),3.7 statistics showed little dependence on H-sigma and remained relatively stable over the whole range of H-sigma values. Potential contributing causes to the substantial r (sub e),3.7 and r (sub e),2.1 differences are discussed. In particular, based on both 1-D and 3-D radiative transfer simulations, we have elucidated mechanisms by which cloud heterogeneity and 3-D radiative effects can cause large differences between r (sub e),3.7 and r (sub e),2.l retrievals for highly inhomogeneous clouds. Our results suggest that the contrast in observed delta r (sub e)3.7-2.1 between cloud regimes is correlated with increases in both cloud r (sub e) and H-sigma. We also speculate that in some highly inhomogeneous drizzling clouds, vertical structure induced by drizzle and 3-D radiative effects might operate together to cause dramatic differences between r (sub e),3.7 and r (sub e),2.1 retrievals.

Platnick, Steven↗