Quantifying Uncertainties in Land Surface Microwave Emissivity Retrievals

Uncertainties in the retrievals of microwave land surface emissivities were quantified over two types of land surfaces: desert and tropical rainforest. Retrievals from satellite-based microwave imagers, including SSM/I, TMI and AMSR-E, were studied. Our results show that there are considerable differences between the retrievals from different sensors and from different groups over these two land surface types. In addition, the mean emissivity values show different spectral behavior across the frequencies. With the true emissivity assumed largely constant over both of the two sites throughout the study period, the differences are largely attributed to the systematic and random errors in the retrievals. Generally these retrievals tend to agree better at lower frequencies than at higher ones, with systematic differences ranging 1~4% (3~12 K) over desert and 1~7% (3~20 K) over rainforest. The random errors within each retrieval dataset are in the range of 0.5~2% (2~6 K). In particular, at 85.0/89.0 GHz, there are very large differences between the different retrieval datasets, and within each retrieval dataset itself. Further investigation reveals that these differences are mostly likely caused by rain/cloud contamination, which can lead to random errors up to 10~17 K under the most severe conditions

Harmonization of remote sensing land surface products : correction of clear-sky bias and characterization of directional effects

Tese de doutoramento, Ciências Geofísicas e da Geoinformação (Deteção Remota), Universidade de Lisboa, Faculdade de Ciências, 2018Land surface temperature (LST) is the mean radiative skin temperature of an area of land resulting from the mean energy balance at the surface. LST is an important climatological variable and a diagnostic parameter of land surface conditions, since it is the primary variable determining the upward thermal radiation and one of the main controllers of sensible and latent heat fluxes between the surface and the atmosphere. The reliable and long-term estimation of LST is therefore highly relevant for a wide range of applications, including, amongst others: (i) land surface model validation and monitoring; (ii) data assimilation; (iii) hydrological applications; and (iv) climate monitoring. Remote sensing constitutes the most effective method to observe LST over large areas and on a regular basis. Satellite LST products generally rely on measurements in the thermal infrared (IR) atmospheric window, i.e., within the 8-13 micrometer range. Beside the relatively weak atmospheric attenuation under clear sky conditions, this band includes the peak of the Earth’s spectral radiance, considering surface temperature of the order of 300K (leading to maximum emission at approximately 9.6 micrometer, according to Wien’s Displacement Law). The estimation of LST from remote sensing instruments operating in the IR is being routinely performed for nearly 3 decades. Nevertheless, there is still a long list of open issues, some of them to be addressed in this PhD thesis. First, the viewing position of the different remote sensing platforms may lead to variability of the retrieved surface temperature that depends on the surface heterogeneity of the pixel – dominant land cover, orography. This effect introduces significant discrepancies among LST estimations from different sensors, overlapping in space and time, that are not related to uncertainties in the methodologies or input data used. Furthermore, these directional effects deviate LST products from an ideally defined LST, which should correspond to the ensemble directional radiometric temperature of all surface elements within the FOV. In this thesis, a geometric model is presented that allows the upscaling of in situ measurements to the any viewing configuration. This model allowed generating a synthetic database of directional LST that was used consistently to evaluate different parametric models of directional LST. Ultimately, a methodology is proposed that allows the operational use of such parametric models to correct angular effects on the retrieved LST. Second, the use of infrared data limits the retrieval of LST to clear sky conditions, since clouds “close” the atmospheric window. This effect introduces a clear-sky bias in IR LST datasets that is difficult to quantify since it varies in space and time. In addition, the cloud clearing requirement severely limits the space-time sampling of IR measurements. Passive microwave (MW) measurements are much less affected by clouds than IR observations. LST estimates can in principle be derived from MW measurements, regardless of the cloud conditions. However, retrieving LST from MW and matching those estimations with IR-derived values is challenging and there have been only a few attempts so far. In this thesis, a methodology is presented to retrieve LST from passive MW observations. The MW LST dataset is examined comprehensively against in situ measurements and multiple IR LST products. Finally, the MW LST data is used to assess the spatial-temporal patterns of the clear-sky bias at global scale.Fundação para a Ciência e a Tecnologia, SFRH/BD/9646

A Physical Model to Estimate Snowfall over Land using AMSU-B Observations

In this study, we present an improved physical model to retrieve snowfall rate over land using brightness temperature observations from the National Oceanic and Atmospheric Administration's (NOAA) Advanced Microwave Sounder Unit-B (AMSU-B) at 89 GHz, 150 GHz, 183.3 +/- 1 GHz, 183.3 +/- 3 GHz, and 183.3 +/- 7 GHz. The retrieval model is applied to the New England blizzard of March 5, 2001 which deposited about 75 cm of snow over much of Vermont, New Hampshire, and northern New York. In this improved physical model, prior retrieval assumptions about snowflake shape, particle size distributions, environmental conditions, and optimization methodology have been updated. Here, single scattering parameters for snow particles are calculated with the Discrete-Dipole Approximation (DDA) method instead of assuming spherical shapes. Five different snow particle models (hexagonal columns, hexagonal plates, and three different kinds of aggregates) are considered. Snow particle size distributions are assumed to vary with air temperature and to follow aircraft measurements described by previous studies. Brightness temperatures at AMSU-B frequencies for the New England blizzard are calculated using these DDA calculated single scattering parameters and particle size distributions. The vertical profiles of pressure, temperature, relative humidity and hydrometeors are provided by MM5 model simulations. These profiles are treated as the a priori data base in the Bayesian retrieval algorithm. In algorithm applications to the blizzard data, calculated brightness temperatures associated with selected database profiles agree with AMSU-B observations to within about +/- 5 K at all five frequencies. Retrieved snowfall rates compare favorably with the near-concurrent National Weather Service (NWS) radar reflectivity measurements. The relationships between the NWS radar measured reflectivities Z(sub e) and retrieved snowfall rate R for a given snow particle model are derived by a histogram matching technique. All of these Z(sub e)-R relationships fall in the range of previously established Z(sub e)-R relationships for snowfall. This suggests that the current physical model developed in this study can reliably estimate the snowfall rate over land using the AMSU-B measured brightness temperatures

Assessing Global Surface Water Inundation Dynamics Using Combined Satellite Information from SMAP, AMSR2 and Landsat

A method to assess global land surface water (fw) inundation dynamics was developed by exploiting the enhanced fw sensitivity of L-band (1.4 GHz) passive microwave observations from the Soil Moisture Active Passive (SMAP) mission. The L-band fw (fw(sub LBand)) retrievals were derived using SMAP H-polarization brightness temperature (Tb) observations and predefined L-band reference microwave emissivities for water and land endmembers. Potential soil moisture and vegetation contributions to the microwave signal were represented from overlapping higher frequency (Tb) observations from AMSR2. The resulting (fw(sub LBand)) global record has high temporal sampling (1-3 days) and 36-km spatial resolution. The (fw(sub LBand)) annual averages corresponded favourably (R=0.84, p<0.001) with a 250-m resolution static global water map (MOD44W) aggregated at the same spatial scale, while capturing significant inundation variations worldwide. The monthly (fw(sub LBand)) averages also showed seasonal inundation changes consistent with river discharge records within six major US river basins. An uncertainty analysis indicated generally reliable (fw(sub LBand)) performance for major land cover areas and under low to moderate vegetation cover, but with lower accuracy for detecting water bodies covered by dense vegetation. Finer resolution (30-m) (fw(sub LBand)) results were obtained for three sub-regions in North America using an empirical downscaling approach and ancillary global Water Occurrence Dataset (WOD) derived from the historical Landsat record. The resulting 30-m (fw(sub LBand)) retrievals showed favourable spatial accuracy for water (70.71%) and land (98.99%) classifications and seasonal wet and dry periods when compared to independent water maps derived from Landsat-8 imagery. The new (fw(sub LBand)) algorithms and continuing SMAP and AMSR2 operations provide for near real-time, multi-scale monitoring of global surface water inundation dynamics and potential flood risk

Potential of satellite-based land emissivity estimates for the detection of high-latitude freeze and thaw states

Reliable detection of freeze and thaw (FT) states is crucial for the terrestrial water cycle, biogeochemical transitions, carbon and methane feedback to the atmosphere, and for the surface energy budget and its associated impacts on the global climate system. This paper is novel in that for the first time a unique approach to examine the potential of passive microwave remotely sensed land emissivity and its added-values of being free from the atmospheric effects and being sensitive to surface characteristics is being applied to the detection of FT states for latitudes north of 35°N. Since accurate characterizations of the soil state are highly dependent on land cover types, a novel threshold-based approach specific to different land cover types is proposed for daily FT detection from the use of three years (August 2012 – July 2015) of the Advanced Microwave Scanning Radiometer – 2 land emissivity estimates. Ground-based soil temperature observations are used as reference to develop threshold values for FT states. Preliminary evaluation of the proposed approach with independent ground observations over Alaska for the year 2015 shows that the use of land emissivity estimates for high-latitude FT detection is promising

Fast Radiative Transfer Approximating Ice Hydrometeor Orientation and its Implication on IWP Retrievals

The accurate simulation of microwave observations of clouds and precipitation are com-putationally challenging. A common simplification is the assumption of totally random orientation (TRO); however, studies have revealed that TRO occurs relatively infrequently in reality. A more appropriate assumption is that of azimuthally random orientation (ARO), but so far it has been a com-putationally expensive task. Recently a fast approximate approach was introduced that incorporates hydrometeor orientation into the assimilation of data from microwave conically scanning instruments. The approach scales the extinction in vertical (V) and horizontal (H) polarised channels to approximate ARO. In this study, the application of the approach was extended to a more basic radiative transfer perspective using the Atmospheric Radiative Transfer Simulator and the high-frequency channels of the Global Precipitation Measurement Microwave Imager (GMI). The comparison of forward simulations and GMI observations showed that with a random selection of scaling factors from a uniform distribution between 1 and 1.4–1.5, it is possible to mimic the full distribution of observed polarisation differences at 166 GHz over land and water. The applicability of this model at 660 GHz was also successfully demonstrated by means of existing airborne data. As a complement, a statistical model for polarised snow emissivity between 160 and 190 GHz was also developed. Combining the two models made it possible to reproduce the polarisation signals that were observed over all surface types, including snow and sea ice. Further, we also investigated the impact of orientation on the ice water path (IWP) retrievals. It has been shown that ignoring hydrometeor orientation has a significant negative impact (∼20% in the tropics) on retrieval accuracy. The retrieval with GMI observations produced highly realistic IWP distributions. A significant highlight was the retrieval over snow covered regions, which have been neglected in previous retrieval studies. These results provide increased confidence in the performance of passive microwave observation simulations and mark an essential step towards developing the retrievals of ice hydrometeor properties based on data from GMI, the Ice Cloud Imager (ICI) and other conically scanning instruments

Improving satellite measurements of clouds and precipitation using machine learning

Observing and measuring clouds and precipitation is essential for climate science, meteorology, and an increasing range of societal and economic activities. This importance is due to the role of clouds and precipitation in the hydrological cycle and the weather and climate of the Earth. Furthermore, patterns of cloudiness and precipitation interact across continental scales and are highly variable in both space and time. Therefore their study and monitoring require observations with global coverage and high temporal resolution, which currently can only be provided by satellite observations.Inferring properties of clouds or precipitation from satellite observations is a non-trivial task. Due to the limited information content of the observations and the complex physics of the atmosphere, such retrievals are endowed with significant uncertainties. Traditional methods to perform these retrievals trade-off processing speed against accuracy and the ability to characterize the uncertainties in their predictions.This thesis develops and evaluates two neural-network-based methods for performing retrievals of hydrometeors, i.e., clouds and precipitation, that are capable of providing accurate predictions of the retrieval uncertainty. The practicality and benefits of the proposed methods are demonstrated using three real-world retrieval applications of cloud properties and precipitation. The demonstrated benefits of these methods over traditional retrieval methods led to the adoption of one of the algorithms for operational use at the European Organisation for the Exploitation of Meteorological Satellites. The two other algorithms are planned to be integrated into the operational processing at the Brazilian National Institute for Space Research, as well as the processing of observations from the Global Precipitation Measurement, a joint satellite mission by NASA and the Japanese Aerospace Exploration Agency.The principal advantage of the proposed methods is their simplicity and computational efficiency. A minor modification of the architecture and training of conventional neural networks is sufficient to capture the dominant source of uncertainty for remote sensing retrievals. As shown in this thesis, deep neural networks can significantly improve the accuracy of satellite retrievals of hydrometeors. With the proposed methods, the benefits of modern neural network architectures can be combined with reliable uncertainty estimates, which are required to improve the characterization of the observed hydrometeors

Exploring the limits of variational passive microwave retrievals

2017 Summer.Includes bibliographical references.Passive microwave observations from satellite platforms constitute one of the most important data records of the global observing system. Operational since the late 1970s, passive microwave data underpin climate records of precipitation, sea ice extent, water vapor, and more, and contribute significantly to numerical weather prediction via data assimilation. Detailed understanding of the observation errors in these data is key to maximizing their utility for research and operational applications alike. However, the treatment of observation errors in this data record has been lacking and somewhat divergent when considering the retrieval and data assimilation communities. In this study, some limits of passive microwave imager data are considered in light of more holistic treatment of observation errors. A variational retrieval, named the CSU 1DVAR, was developed for microwave imagers and applied to the GMI and AMSR2 sensors for ocean scenes. Via an innovative method to determine forward model error, this retrieval accounts for error covariances across all channels used in the iteration. This improves validation in more complex scenes such as high wind speed and persistently cloudy regimes. In addition, it validates on par with a benchmark dataset without any tuning to in-situ observations. The algorithm yields full posterior error diagnostics and its physical forward model is applicable to other sensors, pending intercalibration. This retrieval is used to explore the viability of retrieving parameters at the limits of the available information content from a typical microwave imager. Retrieval of warm rain, marginal sea ice, and falling snow are explored with the variational retrieval. Warm rain retrieval shows some promise, with greater sensitivity than operational GPM algorithms due to leveraging CloudSat data and accounting for drop size distribution variability. Marginal sea ice is also detected with greater sensitivity than a standard operational retrieval. These studies ultimately show that while a variational algorithm maximizes the effective signal to noise ratio of these observations, hard limitations exist due to the finite information content afforded by a typical microwave imager

Exploratory study for detecting low clouds (base < 10,000 feet) over the southwestern United States using Tropical Rainfall Measuring Mission Microwave (TRMM) Imager 85.5 GHz data and coincident 10.8 micron infrared data

Includes bibliographical references.Recent research in retrieving cloud liquid water over land using the 85.5 GHz microwave channel has shown limited success. This work usually requires extensive manipulation of the data to correct for atmospheric effects, and to eliminate rain events Even with these corrections, the over-land methods must still address the complex spatial variability of soil and vegetation characteristics, which have a profound affect on surface emissivity, e.g., a non-uniform background. This work uses the Normalized Polarization Difference (NPD) method in an attempt to identify low cloud signature over the Southwestern United States from 1 June to 31 August 1998. This will provide nighttime capability in identifying low-cloud areas over data-sparse, data-denied regions with relatively uniform terrain characteristics. The development of a simplified method for use in data-sparse, data-denied regions was of prime importance In order to identify low clouds, effective surface emittance calculations were made using co-located Tropical Rainfall Measuring Mission Microwave 85.5 GHz data and coincident 10.8 μm infrared data for clear-sky conditions. Based on previous work, the Southwestern United States, in general, should have the large polarization differences (> 0.015) as well as uniform skin temperatures, which could provide a suitable background to detect low cloud signal above the background noise. Eleven sites were chosen based on varying degrees of polarization difference, as well as having available surface and upper air data. In situ surface observations were used to identify the low cloud base, while the infrared brightness temperature at 10. 8 μm was used to estimated the cloud top height using the nearest upper air sounding. The estimated cloud thickness was calculated from this data. Extensive efforts were made to eliminate multiple cloud layers, which would have a negative impact on brightness temperatures. A scattering index, the Grody algorithm, and surface observations were used to filter precipitating clouds. The results using a linear regression best fit indicated poor correlation (R2) between the NPD and the 2 estimated low-cloud thickness with values of R2 ranging from 0.002 to 0.345. Four primary error mechanisms were identified, and quantified. The uncorrected atmosphere accounted for about a 0.7-1.7 K error; horizontal variations in infrared temperature on the scale of 2.0-7.3 K; instrument noise of about 1.5K; and effective surface emissivity relative uncertainties ranging from 0.22- 1.16%. Future improvements in sensor noise characteristics and resolution, as well as the ability to perform instantaneous atmospheric corrections using coincident sounder and microwave imager data should lead to a viable NPD method over land.Research supported by the Department of Defense Center for Geosciences/Atmospheric Research, under the Army Research Laboratory (ARL) Cooperative Agreement no. DAAL01-98-2-0078, and by the Air Force Institute of Technology (AFIT)

Amélioration de la caractérisation de la neige et du sol arctique afin d’améliorer la prédiction de l’équivalent en eau de la neige en télédétection micro-ondes

Le phénomène de l’amplification arctique consiste en une augmentation plus prononcée des températures de surface dans cette région que sur le reste du globe. Ce phénomène est notamment dû à la diminution marquée du couvert nival provoquant un déséquilibre dans le bilan d’énergie de surface via une réduction généralisée de l’albédo (rétroaction positive). L’accélération du réchauffement est jusqu’à trois fois plus élevée dans ces régions. Il est donc primordial, dans un contexte de changement climatique arctique, de poursuivre et d’améliorer le suivi à grande échelle du couvert nival afin de mieux comprendre les processus gouvernant la variabilité spatio-temporelle du manteau neigeux. Plus spécifiquement, l’Équivalent en Eau de la Neige (EEN) est généralement utilisé pour quantifier deux propriétés (hauteur et densité) de la neige. Son estimation à grande échelle dans les régions éloignées tel que l’Arctique provient actuellement essentiellement de produits en micro-ondes passives satellitaires. Cependant, il existe encore beaucoup d’incertitudes sur les techniques d’assimilation de l’ÉEN par satellite et ce projet vise une réduction de l’erreur liée à l’estimation de l’ÉEN en explorant deux des principales sources de biais tels que : 1) la variabilité spatiale de l’épaisseur et des différentes couches du manteau neigeux arctique liées à la topographie et la végétation au sol influençant l’estimation de l’ÉEN; et 2) les modèles de transfert radiatif micro-ondes de la neige et du sol ne bénéficient pas actuellement d’une bonne paramétrisation en conditions arctiques, là où les erreurs liées à l’assimilation de l’ÉEN sont les plus importantes. L’objectif global est donc d’analyser les propriétés géophysiques du couvert nival en utilisant des outils de télédétection et de modélisation pour diminuer l’erreur liée à la variabilité spatiale locale dans l’estimation du ÉEN à grande échelle, tout en améliorant la compréhension des processus locaux qui affectent cette variabilité. Premièrement, une analyse haute résolution à l’aide de l’algorithme Random Forest a permis de prédire la hauteur de neige à une résolution spatiale de 10 m avec une RMSE de 8 cm (23%) et d’en apprendre davantage sur les processus de distribution de la neige en Arctique. Deuxièmement, la variabilité du manteaux neigeux arctique (hauteur et microstructure) a été incorporée dans des simulations en transfert radiatif micro-ondes de la neige et comparée au capteur satellitaire SSMIS. L’ajout de variabilité améliore la RMSE des simulations de 8K par rapport à un manteau neigeux uniforme. Finalement, une paramétrisation du sol gelé est présentée à l’aide de mesures de rugosité provenant de photogrammétrie (Structure-from-Motion). Cela a permis d’investiguer trois modèles de réflectivité micro-ondes du sol ainsi que la permittivité effective du sol gelé avec une rugosité SfM d’une précision de 0.1 mm. Ces données de rugosité SfM avec une permittivité optimisée (ε'_19 = 3.3, ε'_37 = 3.6) réduisent significativement l’erreur des températures de brillance simulées par rapport à des mesures au sols (RMSE = 3.1K, R^2 = 0.71) pour toutes les fréquences et polarisations. Cette thèse offre une caractérisation des variables de surface (neige et sol) en Arctique en transfert radiatif micro-ondes qui bénéficie aux multiples modélisations (climatiques et hydrologiques) de la cryosphère