On the equivalence between radiance and retrieval assimilation

The need for consistent assimilation of satellite measurements for numerical weather prediction led operational meteorological centers to assimilate satellite radiances directly using variational data assimilation systems. More recently there has been a renewed interest in assimilating satellite retrievals (e.g., to avoid the use of relatively complicated radiative transfer models as observation operators for data assimilation). The aim of this paper is to provide a rigorous and comprehensive discussion of the conditions for the equivalence between radiance and retrieval assimilation. It is shown that two requirements need to be satisfied for the equivalence: (i) the radiance observation operator needs to be approximately linear in a region of the state space centered at the retrieval and with a radius of the order of the retrieval error; and (ii) any prior information used to constrain the retrieval should not underrepresent the variability of the state, so as to retain the information content of the measurements. Both these requirements can be tested in practice. When these requirements are met, retrievals can be transformed so as to represent only the portion of the state that is well constrained by the original radiance measurements and can be assimilated in a consistent and optimal way, by means of an appropriate observation operator and a unit matrix as error covariance. Finally, specific cases when retrieval assimilation can be more advantageous (e.g., when the estimate sought by the operational assimilation system depends on the first guess) are discussed

Development and evaluation of an advanced microwave radiance data assimilation system for estimating snow water storage at the continental scale

Snow cover modulates the Earth's surface energy and water fluxes, and snowmelt runoff is the principal source of water for humans and ecosystems in many of the middle to high latitudes in the Northern Hemisphere. Understanding spatial and temporal variation in snowpack is crucial for climate studies and water resource management and thus the climate and hydrological research communities have invested in improving large-scale snow estimates. This dissertation aims to develop an advanced snow radiance assimilation (RA) system to improve continental-scale snow water storage estimates. The RA system is comprised of the Community Land Model version 4 (CLM4) (for snow energy and mass balance modeling), radiative transfer models (RTMs) (for brightness temperature estimates), and the Data Assimilation Research Testbed (DART) (for ensemble-based data assimilation). Two snowpack RTMs, the Microwave Emission Model for Layered Snowpacks (MEMLS) and the Dense Media Radiative Transfer--Multi Layers model (DMRT-ML), are used to simulate T[subscript B] of a multi-layered snowpack. Through an error characterization study, this dissertation presents that the correlations between snow water equivalent (SWE) error and brightness temperature (T[subscript B]) error and subsequent RA performance in estimating snow are significantly affected by all physical properties of soil and snow involved in estimating T[subscript B]. Based on the error characterization results, it is hypothesized that the continental-scale RA performance in estimating snow water storage can be improved by simultaneously updating all model physical states and parameters determining T[subscript B] based on a rule, in which prior estimates are updated depending on their correlations with a prior T[subscript B]. The results of a series of RA experiments show that the improved continental-scale snow estimates are obtained by applying the hypothesis. This dissertation also shows that further improvement of the performance of the RA system can be achieved, especially for vegetated areas, by assimilating the best-performing frequency channels (i.e., 18.7 and 23.8 GHz) and by considering the vegetation single scattering albedo to represent the vegetation effect on T[subscript B] at the top of the atmosphere.Geological Science

Reduced major axis approach for correcting GPM/GMI radiometric biases to coincide with radiative transfer simulation

Correcting radiometric biases is crucial prior to the use of satellite observations in a physically based retrieval or data assimilation system. This study proposes an algorithm - RARMA (Radiometric Adjustment using Reduced Major Axis) for correcting the radiometric biases so that the observed radiances coincide with the simulation of a radiative transfer modeL The RARMA algorithm is a static bias correction algorithm, which is developed using the reduced major axis (RMA) regression approach, NOAA\u27s Community Radiative Transfer Model (CRTM) has been used as the basis of radiative transfer simulation for adjusting the observed radiometric biases. The algorithm is experimented and applied to the recently launched Global Precipitation Measurement (GPM) mission\u27s GPM Microwave Imager (GMI), Experimental results demonstrate that radiometric biases are apparent in the GMI instrument, The RARMA algorithm has been able to correct such radiometric biases and a significant reduction of observation residuals is revealed while assessing the performance of the algorithm, The experiment is currently tested on clear scenes and over the ocean surface, where, surface emissivity is relatively easier to model. with the help of a microwave emissivity model (FASTEM-5)

Development of airborne hemispheric spectrometer

A new concept of hyperspectral instrument is presented. Novel design of hyperspectral skydome allows retrieval of atmospheric constituents and properties from a snapshot of spectral solar radiation over entire sky, regardless of platform motion either on ground or aircraft. Design and description of subsystems of the instrument are given followed by preliminary tolerance analysis, whose results are to be added in the retrieval algorithm along with hardware specifications. Extended application of the hyperspectral skydome is being carried out filling in the gap in the imaging spectrometry

Towards a Real-Time Data Driven Wildland Fire Model

A wildland fire model based on semi-empirical relations for the spread rate of a surface fire and post-frontal heat release is coupled with the Weather Research and Forecasting atmospheric model (WRF). The propagation of the fire front is implemented by a level set method. Data is assimilated by a morphing ensemble Kalman filter, which provides amplitude as well as position corrections. Thermal images of a fire will provide the observations and will be compared to a synthetic image from the model state.Comment: 5 pages, 4 figure

Benchmarking clear-sky reflectances

Accurate calculations of shortwave reflectances in clear-sky aerosol-laden atmospheres are necessary for various applications in atmospheric sciences. However, computational cost becomes increasingly important for some applications such as data assimilation of top-of-atmosphere reflectances in models of atmospheric composition. This study aims to provide a benchmark that can help in assessing these two requirements in combination. We describe a protocol and input data for 44 080 cases involving various solar and viewing geometries, four different surfaces (one oceanic bidirectional reflectance function and three albedo values for a Lambertian surface), eight aerosol optical depths, five wavelengths, and four aerosol types. We first consider two models relying on the discrete ordinate method: VLIDORT (in vector and scalar configurations) and DISORT (scalar configuration only). We use VLIDORT in its vector configuration as a reference model and quantify the loss of accuracy due to (i) neglecting the effect of polarization in DISORT and VLIDORT (scalar) models and (ii) decreasing the number of streams in DISORT. We further test two other models: the 6SV2 model, relying on the successive orders of scattering method, and Forward-Lobe Two-Stream Radiance Model (FLOTSAM), a new model under development by two of the authors. Typical mean fractional errors of 2.8 % and 2.4 % for 6SV2 and FLOTSAM are found, respectively. Computational cost depends on the input parameters but also on the code implementation and application as some models solve the radiative transfer equations for a range of geometries while others do not. All necessary input and output data are provided as a Supplement as a potential resource for interested developers and users of radiative transfer models

Exploring RTTOV to retrieve land surface temperature from a geostationary satellite constellation

Tese de mestrado em Ciência geofísicas, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2013A constelação de satélites meteorológicos de órbita geostacionária, actualmente em funcionamento operacional, abre perspectivas interessantes para a monitorização de propriedades da superfície terrestre, nomeadamente as caracterizadas por elevada variabilidade temporal. De entre estas, destaca-se a temperatura da superfície do solo cujo ciclo diurno pode ser resolvido através de uma amostragem de alta frequência que, no caso da actual constelação de satélites geostacionários, varia no intervalo de 15 minutos até uma hora. No entanto, a determinação da temperatura à superfície a partir da constelação de satélites geostacionários implica ultrapassar algumas dificuldades relacionadas com as características dos diferentes radiómetros a bordo dos diferentes satélites, podendo mencionar-se, a título de exemplo, o número distinto de canais na janela do infravermelho e a diferente largura de banda dos próprios canais disponíveis, características essas que constituem constrangimentos ao tipo de algoritmo comum a utilizar para a determinação da temperatura da superfície do solo. Os modelos de transferência radiativa constituem uma ferramenta indispensável quando se pretende desenvolver e testar a qualidade de um dado algoritmo de determinação da temperatura da superfície do solo. Neste particular, o modelo RTTOV é capaz de simular radiâncias no topo da atmosfera e perfis verticais da transmissividade da atmosfera, desde que se conheçam os respectivos perfis atmosféricos de temperatura e humidade, bem como as concentrações dos gases activos radiativamente que constituem a atmosfera seca e outras propriedades relativas às nuvens e à superfície do solo. De forma a justificar a utilização do modelo RTTOV nesta dissertação, procede-se a uma avaliação sistemática do desempenho do RTTOV através do cálculo da radiância no topo da atmosfera e a da transmissividade da atmosfera, simuladas para uma vasta gama de perfis atmosféricos com o RTTOV, valores esses que são, em seguida, comparados com simulações, obtidas para condições idênticas, recorrendo ao modelo MODTRAN. Os resultados obtidos apontam para uma tendência para valores mais frios na temperatura de brilho simulada com o modelo RTTOV, relativamente ao modelo MODTRAN. Em termos de transmissividade, o modelo RTTOV apresenta, em geral, valores mais elevados que os do MODTRAN. Pode, assim, concluir-se que o RTTOV apresenta um comportamento mais “transparente” que o MODTRAN. O valor da raiz do erro quadrático médio (REQM) da transmissividade é de 0.04, que não difere em ordem de grandeza do valor do viés, de 0.03, sugerindo que a “transparência” do modelo RTTOV é sistemática e não aleatória. Uma REQM de 0.04 na transmissividade poderá ser relevante no caso de atmosferas secas, onde se podem atingir erros até 20%. Por outro lado, a REQM (viés) da temperatura de brilho no topo da atmosfera é de 0.21K (-0.04K), tendo-se neste caso que o erro é de cariz aleatório, e os erros máximos são da ordem de 0.1%. Estes resultados sugerem que o modelo RTTOV está de acordo com o modelo MODTRAN para a maioria dos casos, com excepção daqueles cuja atmosfera seja mais opaca, onde os erros na transmissividade podem ser relevantes. No entanto, o desempenho do modelo RTTOV é aceitável para aplicações em tempo quase real, especialmente tendo em conta o desempenho em termos de tempos de corrida, quando comparado com o MODTRAN. No presente trabalho, explora-se o potencial do RTTOV como ferramenta computacionalmente eficiente para inverter a equação de transferência radiativa num canal centrado em 10.8 μm, e como instrumento de base para o desenvolvimento de um mono-canal estatístico para determinar a temperatura da superfície do solo. Começa por proceder-se a uma avaliação do desempenho do RTTOV para uma gama vasta de condições atmosféricas e de superfícies do solo. Para tal, gera-se um elevado número de temperaturas de brilho sintéticas recorrendo a uma base de dados constituída por mais de 10 000 perfis atmosféricos, juntamente com uma ampla variedade de valores de emissividade à superfície e de ângulos de visão do satélite. Em seguida, relaciona-se as radiâncias obtidas (no topo da atmosfera) com as respectivas temperaturas à superfície, utilizando coeficientes obtidos estatisticamente através de regressões lineares. A fim de estimar o erro na determinação da temperatura da superfície do solo, assumem-se incertezas nos dados de entrada, nomeadamente uma componente de incerteza associada ao ruído do radiómetro a bordo do satélite, uma outra componente associada à emissividade da superfície do solo e uma terceira associada aos perfis de temperatura e humidade, sendo, neste último caso, a incerteza estimada a partir das matrizes de covariância dos erros do modelo operacional do Centro de Previsão do Tempo a Prazo Médio. Os resultados obtidos mostram que a temperatura da superfície do solo estimada apresenta uma raiz do erro quadrático médio de 1.4K quando se utiliza o método físico baseado no RTTOV, valor este que aumenta em cerca de 15%, para 1.6K, quando se recorre ao método estatístico, baseado em regressões lineares. Quando subdivididos em classes de vapor de água na atmosfera e na emissividade do solo, o método físico apresenta sempre um desempenho ligeiramente superior, com excepção do caso de atmosferas húmidas conjugadas com elevada emissividade da superfície do solo, onde o desempenho do método estatístico apresenta um valor mais baixo da raiz do erro quadrático médio. Se bem que, em termos globais, o método físico apresente um desempenho superior ao método estatístico, a simplicidade deste último método aliada ao facto de o primeiro requerer um conhecimento preciso de termos da equação de transferência radiativa que são, normalmente, difíceis de estimar por serem sensíveis às incertezas no perfil da atmosfera abre perspectivas interessantes para a utilização, de forma operacional e em tempo quase real, do método estatístico para determinar a temperatura da superfície do solo com base em informação fornecida por uma constelação de satélites geostacionarios. Neste contexto se antevê que o método estatístico desenvolvido na presente dissertação venha a constituir a base para o desenvolvimento de um novo produto operacional de determinação da temperatura da superfície do solo baseado nos satélites GOES e MTSAT a desenvolver no contexto do “Copernicus Global Land Service”.The geostationary constellation of meteorological satellites that is currently in operations opens interesting perspectives in the monitoring of surface properties that are characterized by a high variability in time. One of such properties is the Land Surface Temperature whose daily cycle may be retrieved thanks to the high frequency sampling that presently ranges from 15 min to 1 hour. Retrieval of LST from the geostationary constellation implies nevertheless solving for a number of difficulties that are linked to the radiometers on-board the different satellites, namely the range and number of available channels in the infrared window which constrain the algorithms to be used to retrieve LST. Radiative transfer models are an indispensable tool when developing and assessing the quality of a given algorithm for LST retrieval. The so-called RTTOV (Radiative Transfer for TOVS) model is a fast radiative transfer model that is able to simulate radiances when given an atmospheric profile of temperature, variable gas concentrations, cloud and surface properties. In the present work, we explore the potential of RTTOV both as a computationally-efficient means to invert the equation of radiative transfer in a channel centered about 10.8 μm and as a basis of development of a statistical mono-window procedure to retrieve LST. First the performance of RTTOV is evaluated for a very wide range of conditions. For this purpose a large number of synthetic values of brightness temperature are generated using a dataset of more than 10,000 atmospheric profiles, together with a wide variety of values of surface emissivity and viewing angles. Obtained top-of-atmosphere radiances are then related with LST using coefficients statistically obtained by means of linear regressions. In order to estimate the error of LST retrievals assuming realistic uncertainties in the input data, an uncertainty component was added to surface emissivity as well as to temperature and humidity profiles using appropriate error covariance matrices. The top-of-atmosphere brightness temperatures were further perturbed according to the sensor expected noise. Obtained results show that estimated LST presents RMSE (with respect to reference values) of 1.4K when using the physically-based approach based on RTTOV, and of 1.6K when using a statistically-based mono-window procedure

Comparison of cloud products within IASI footprints for the assimilation of cloudy radiances

This article compares different methods of deriving cloud properties in the footprint of the Infrared Atmospheric Sounding Interferometer (IASI), onboard the European MetOp satellite. Cloud properties produced by ten operational schemes are assessed and an intercomparison of the products for a 12 h global acquisition is presented. Clouds cover a large part of the Earth, contaminating most of the radiance data. The estimation of cloud top height and effective amount within the sounder footprint is an important step towards the direct assimilation of cloud-affected radiances. This study first examines the capability of all the schemes to detect and characterize the clouds for all complex situations and provides some indications of confidence in the data. Then the dataset is restricted to thick overcast single layers and the comparison shows a significant agreement between all the schemes. The impact of the retrieved cloud properties on the residuals between calculated cloudy radiances and observations is estimated in the long-wave part of the spectrum

Principal Component-Based Radiative Transfer Model (PCRTM) for Hyperspectral Sensors

Modern infrared satellite sensors such as Atmospheric Infrared Sounder (AIRS), Cosmic Ray Isotope Spectrometer (CrIS), Thermal Emission Spectrometer (TES), Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) and Infrared Atmospheric Sounding Interferometer (IASI) are capable of providing high spatial and spectral resolution infrared spectra. To fully exploit the vast amount of spectral information from these instruments, super fast radiative transfer models are needed. This paper presents a novel radiative transfer model based on principal component analysis. Instead of predicting channel radiance or transmittance spectra directly, the Principal Component-based Radiative Transfer Model (PCRTM) predicts the Principal Component (PC) scores of these quantities. This prediction ability leads to significant savings in computational time. The parameterization of the PCRTM model is derived from properties of PC scores and instrument line shape functions. The PCRTM is very accurate and flexible. Due to its high speed and compressed spectral information format, it has great potential for super fast one-dimensional physical retrievals and for Numerical Weather Prediction (NWP) large volume radiance data assimilation applications. The model has been successfully developed for the National Polar-orbiting Operational Environmental Satellite System Airborne Sounder Testbed - Interferometer (NAST-I) and AIRS instruments. The PCRTM model performs monochromatic radiative transfer calculations and is able to include multiple scattering calculations to account for clouds and aerosols