Retrieving Decadal Climate Change from Satellite Radiance Observations-A 100-year CO2 Doubling OSSE Demonstration.

Preparing for climate change depends on the observation and prediction of decadal trends of the environmental variables, which have a direct impact on the sustainability of resources affecting the quality of life on our planet. The NASA Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission is proposed to provide climate quality benchmark spectral radiance observations for the purpose of determining the decadal trends of climate variables, and validating and improving the long-range climate model forecasts needed to prepare for the changing climate of the Earth. The CLARREO will serve as an in-orbit, absolute, radiometric standard for the cross-calibration of hyperspectral radiance spectra observed by the international system of polar operational satellite sounding sensors. Here, we demonstrate that the resulting accurately cross-calibrated polar satellite global infrared spectral radiance trends (e.g., from the Metop IASI instrument considered here) can be interpreted in terms of temperature and water vapor profile trends. This demonstration is performed using atmospheric state data generated for a 100-year period from 2000-2099, produced by a numerical climate model prediction that was forced by the doubling of the global average atmospheric CO2 over the 100-year period. The vertical profiles and spatial distribution of temperature decadal trends were successfully diagnosed by applying a linear regression profile retrieval algorithm to the simulated hyperspectral radiance spectra for the 100-year period. These results indicate that it is possible to detect decadal trends in atmospheric climate variables from high accuracy all-sky satellite infrared radiance spectra using the linear regression retrieval technique

Spatio-temporal constraints for emissivity and surface temperature retrieval: Preliminary results and comparisons for SEVIRI and IASI observation

Infrared instrumentation on geostationary satellites is now rapidly approaching the spectral quality and accuracy of modern sensors flying on polar platforms. Currently, the core of EUMETSAT geostationary meteorological programme is the Meteosat Second Generation (MSG). However, EUMETSAT is preparing for the Meteosat Third Generation (MTG). The capability of geostationary satellites to resolve the diurnal cycle and hence to provide time-resolved sequences or times series of observations is a source of information which could suitably constrain the derivation of geophysical parameters. Nowadays, also because of lack of time continuity, when dealing with observations from polar platforms, the problem of deriving geophysical parameters is normally solved by considering each single observation as independent of past and future events. For historical reason, the same approach is currently pursued with geostationary observations, which are still being dealt with as they were with polar observations. In this study we show some preliminary results on emissivity and surface temperature retrieval for SEVIRI observations, using the Kalman filter methodology (KF) and compare the retrievals with those obtained using IASI observations co-localized with SEVIRI ones using the times accumulation approach (Optimal Estimation OE). The Sahara desert was chosen as target area, and both SEVIRI and IASI data (infrared radiances and cloud mask) were acquired. The time period considered is that of July 2010 (the whole month). ECMWF analyses for the same date and target area have also been acquired, which comprise Ts, T(p), O(p), Q(p) for the canonical hours 0:00, 6:00, 12:00 and 18:00. Moreover, for the purpose of developing a suitable background for emissivity, the Global Infrared Land Surface Emissivity database developed at CIMSS, University of Wisconsin, derived by MODIS observations was used and was available from the year 2003 till 2011. Concerning the performance of the two methodologies, the retrieval of skin temperature is almost equivalent. The agreement between OE and KF is fairly good if compared with ECMWF analysis for sea surface, while for land surface, OE and KF agree fairly well with ECMWF during the night, but at midday ECMWF shows a cold bias of 10 K and more. For emissivity the comparison with the UW/BFEMIS database for the same date and location is fairly good for both methods

Surface Emissivity Retrieved with Satellite Ultraspectral IR Measurements for Monitoring Global Change

Surface and atmospheric thermodynamic parameters retrieved with advanced ultraspectral remote sensors aboard Earth observing satellites are critical to general atmospheric and Earth science research, climate monitoring, and weather prediction. Ultraspectral resolution infrared radiance obtained from nadir observations provide atmospheric, surface, and cloud information. Presented here is the global surface IR emissivity retrieved from Infrared Atmospheric Sounding Interferometer (IASI) measurements under "clear-sky" conditions. Fast radiative transfer models, applied to the cloud-free (or clouded) atmosphere, are used for atmospheric profile and surface parameter (or cloud parameter) retrieval. The inversion scheme, dealing with cloudy as well as cloud-free radiances observed with ultraspectral infrared sounders, has been developed to simultaneously retrieve atmospheric thermodynamic and surface (or cloud microphysical) parameters. Rapidly produced surface emissivity is initially evaluated through quality control checks on the retrievals of other impacted atmospheric and surface parameters. Surface emissivity and surface skin temperature from the current and future operational satellites can and will reveal critical information on the Earth s ecosystem and land surface type properties, which can be utilized as part of long-term monitoring for the Earth s environment and global climate change

IASI 일차변분법 자료동화 시스템 내 구름 변수 산출 알고리즘 개선과 수치예보 정확도에 미치는 영향

학위논문 (박사) -- 서울대학교 대학원 : 자연과학대학 지구환경과학부, 2020. 8. 손병주.The Unified Model (UM) data assimilation system incorporates a one-dimensional variational (1D-Var) analysis of cloud variables for hyperspectral infrared sounders that allows the assimilation of radiances in cloudy areas. For the Infrared Atmospheric Sounding Interferometer (IASI) radiance assimilation in the UM, a first guess pair of cloud top pressure (CTP) and cloud fraction (CF) is estimated using the minimum residual (MR) method, which simultaneously obtains CTP and CF by minimizing radiances difference between observation and model simulation. In this study, specific pairs of CTP and CF yielding the smallest 1D-Var temperature and humidity analysis error were found from the ECMWF short-range forecast based IASI simulated radiances and background states, and defined as optimum cloud parameters. Compared to the optimum results, it is noted that the MR method tends to overestimate cloud top height while underestimating cloud fraction. This fact necessitates an improved cloud retrieval for better 1D-Var analysis performance. An Artificial Neural Network (ANN) approach was taken to estimate CTP as close as possible to the optimum value, based on the hypothesis that CTP and CF closer to the optimum values will bring in better 1D-Var results. The ANN-based cloud retrievals indicated that CTP and CF biases and root mean square errors against the optimum values shown in the MR method are much reduced. The resultant 1D-Var analysis with new first guess based on the ANN method showed that the errors of temperature and moisture in the mid-troposphere are reduced, due to the use of larger volume of cloud-affected infrared radiances. Furthermore, the computational time can be substantially reduced as much as 1.85% by the ANN method, compared to the MR method. The evaluation of the ANN method in the UM global weather forecasting system demonstrated that it helps to use more infrared radiances in the cloudy-sky data assimilation. Although its impact on the UM global temperature and moisture forecasts was found to be near neutral, it has been demonstrated that the UM global precipitation forecasts and tropical cyclone forecast, which occur mostly around cloud regions, can be improved by the ANN method.기상청 현업 모델인 통합수치모델 (Unified Model) 내 자료동화 과정 중, 적외 초분광 센서인 IASI (Infrared Atmospheric Sounding Interferometer) 관측자료를 활용하는 방법으로는 구름 변수를 1D-Var 과정 내에서 산출하는 Cloudy 1D-Var 방법(Pavelin et al., 2008)이 사용되고 있다. Cloudy 1D-Var 방법에서는 산출된 구름변수(운정고도, 운량)를 이용해 구름지역을 탐지할 뿐만 아니라 자료동화에 사용되는 채널을 선정하기 때문에 운정고도와 운량을 정확하게 산출하는 것은 매우 중요하다. Cloudy 1D-Var 방법에서 구름 변수인 운정고도와 운량의 초기값은 minimum residual (MR) 방법(Eyre and Menzel, 1989)을 통해 관측 복사량과 모델 배경장이 만들어내는 복사량의 차이를 최소화 시키는 값으로 얻어진다. 본 연구에서는 ECMWF 단기예보장을 활용하여 IASI 모의 관측 복사량과 모델 배경장을 생산하였고, 이를 이용해 최종 온도 습도 분석장의 에러를 최소로 만드는 새로운 구름변수들을 찾아내어 이를 최적의 운정고도, 운량으로 정의하였다. 정의한 최적의 구름 변수를 기준으로 MR 방법이 구름의 고도를 상대적으로 상층으로 산출한다는 것을 확인하였고, 이로 인해 온습도 1D-Var 분석장의 에러를 최소화 시키지 못한다는 점을 확인하였다. 온습도 1D-Var 분석장을 개선하기 위해 최적의 구름변수와 가장 가까운 구름변수를 산출할 수 있는 방법을 모색하였고, IASI 적외 관측 복사량과 모델 배경장을 입력 자료로 하여 운정고도를 산출해내는 인공신경망(ANN; Artificial Neural Network) 모델을 개발하였다. 검증을 통해 ANN 모델에서 산출된 운정고도, 운량이 앞서 정의한 최적의 운정고도, 운량과 높은 상관관계를 갖는다는 것을 확인하였고, 구름에 의해 영향을 받은 더 많은 채널들이 자료동화 과정 내에 사용되는 것을 확인하였다. 이와 함께 기존 MR 방법을 사용했을 때 얻어진 1D-Var 온습도 분석장 결과와 비교해보았을 때 모든 층에서 온습도 분석장이 개선되는 것을 볼 수 있었고, 특히 중층에서 온도 에러가 10% 가량 줄어드는 것을 확인하였다. 개발한 ANN 모델을 이용하면 운정고도를 먼저 산출하고, 이를 이용해 운량을 계산한다는 점에서 계산시간을 기존 MR 방법의 1.85%로 줄이는 장점까지 얻을 수 있었다. 또한 새로 개발한 ANN 알고리즘을 실제 UM 내에도 적용시켜 보았는데, 이때도 새롭게 산출된 운정고도가 기존의 MR 방법을 통해 산출되었던 운정고도보다 상대적으로 낮게 산출되면서 더 많은 구름지역 IASI 적외 초분광 채널 정보가 자료동화 과정 내에 사용된다는 것을 확인할 수 있었다. 나아가 새롭게 개발한 ANN 방법이 수치예보 모델 초기장 및 예보장 정확도에 주는 영향도 살펴보았다. 전 지구적 온습도 초기장 및 예보 정확도에 미치는 영향은 미미하게 나타났지만, 주로 구름 지역 주변에서 구름을 동반하여 발생하는 날씨 현상인 강수 및 열대 저기압의 예보정확도가 ANN 방법을 사용함으로써 향상되는 것을 확인할 수 있었다.1. Introduction 1 2. Background and theory 6 2.1. IASI hyperspectral measurement 6 2.2. Theoretical background 10 2.3. Radiance simulation in Radiative Transfer Model 12 3. IASI 1D-Var assimilation 14 3.1. Retrieval of cloud top pressure and cloud fraction 14 3.2. 1D-Var analysis 20 4. Preparation of simulation dataset 21 4.1. ECMWF short-range forecast 21 4.2. Simulation of IASI radiances 25 4.3. Simulation of UM background profiles 32 5. Assessment of pre-developed methods with simulation dataset 35 5.1. Pre-developed cloudy-sky radiance assimilation 35 5.2. Assessment of the pre-developed assimilation method 37 6. Development of a new cloud parameters retrieval method 40 6.1. Definition of 'Optimum CTP' 40 6.2. Evaluation of original retrieval method 48 6.3. New retrieval method with an ANN approach 54 7. Assessment of ANN retrieval method in the 1D-Var analysis 58 7.1. Simulation Framework 58 7.2. Experiments with the UM NWP system 73 8. Impact study of ANN method on the UM forecast 83 8.1. Assessment of experiments in the UM NWP system 85 8.2. Impact on the precipitation forecast 92 8.3. Impact of tropical cyclone forecast 97 9. Summary and discussion 110 References 116 국문초록 121 감사의 글 124Docto

GEWEX water vapor assessment (G-VAP): final report

Este es un informe dentro del Programa para la Investigación del Clima Mundial (World Climate Research Programme, WCRP) cuya misión es facilitar el análisis y la predicción de la variabilidad de la Tierra para proporcionar un valor añadido a la sociedad a nivel práctica. La WCRP tiene varios proyectos centrales, de los cuales el de Intercambio Global de Energía y Agua (Global Energy and Water Exchanges, GEWEX) es uno de ellos. Este proyecto se centra en estudiar el ciclo hidrológico global y regional, así como sus interacciones a través de la radiación y energía y sus implicaciones en el cambio global. Dentro de GEWEX existe el proyecto de Evaluación del Vapor de Agua (VAP, Water Vapour Assessment) que estudia las medidas de concentraciones de vapor de agua en la atmósfera, sus interacciones radiativas y su repercusión en el cambio climático global.El vapor de agua es, de largo, el gas invernadero más importante que reside en la atmósfera. Es, potencialmente, la causa principal de la amplificación del efecto invernadero causado por emisiones de origen humano (principalmente el CO2). Las medidas precisas de su concentración en la atmósfera son determinantes para cuantificar este efecto de retroalimentación positivo al cambio climático. Actualmente, se está lejos de tener medidas de concentraciones de vapor de agua suficientemente precisas para sacar conclusiones significativas de dicho efecto. El informe del WCRP titulado "GEWEX water vapor assessment. Final Report" detalla el estado actual de las medidas de las concentraciones de vapor de agua en la atmósfera. AEMET ha colaborado en la generación de este informe y tiene a unos de sus miembros, Xavier Calbet, como co-autor de este informe

Innovative Techniques for the Retrieval of Earth’s Surface and Atmosphere Geophysical Parameters: Spaceborne Infrared/Microwave Combined Analyses

With the advent of the first satellites for Earth Observation: Landsat-1 in July 1972 and ERS-1 in May 1991, the discipline of environmental remote sensing has become, over time, increasingly fundamental for the study of phenomena characterizing the planet Earth. The goal of environmental remote sensing is to perform detailed analyses and to monitor the temporal evolution of different physical phenomena, exploiting the mechanisms of interaction between the objects that are present in an observed scene and the electromagnetic radiation detected by sensors, placed at a distance from the scene, operating at different frequencies. The analyzed physical phenomena are those related to climate change, weather forecasts, global ocean circulation, greenhouse gas profiling, earthquakes, volcanic eruptions, soil subsidence, and the effects of rapid urbanization processes. Generally, remote sensing sensors are of two primary types: active and passive. Active sensors use their own source of electromagnetic radiation to illuminate and analyze an area of interest. An active sensor emits radiation in the direction of the area to be investigated and then detects and measures the radiation that is backscattered from the objects contained in that area. Passive sensors, on the other hand, detect natural electromagnetic radiation (e.g., from the Sun in the visible band and the Earth in the infrared and microwave bands) emitted or reflected by the object contained in the observed scene. The scientific community has dedicated many resources to developing techniques to estimate, study and analyze Earth’s geophysical parameters. These techniques differ for active and passive sensors because they depend strictly on the type of the measured physical quantity. In my P.h.D. work, inversion techniques for estimating Earth’s surface and atmosphere geophysical parameters will be addressed, emphasizing methods based on machine learning (ML). In particular, the study of cloud microphysics and the characterization of Earth’s surface changes phenomenon are the critical points of this work

Development of Regionally Focused Algorithm for AIRS Temperature and Humidity Retrievals Using a Moving-Window Technique

학위논문 (박사)-- 서울대학교 대학원 자연과학대학 지구환경과학부, 2017. 8. 손병주.Regionally focused algorithm for Atmospheric Infrared Sounder (AIRS) temperature and humidity retrievals was developed. We first employed regression model with a moving window technique. This is done by relating the AIRS measurements to temperature and humidity profiles with consideration of regionally and seasonally changing local climatology. Regression coefficients were obtained from four-year (2006-2009) of ECMWF interim data over East Asia and simulated AIRS radiances. Result showing a notable improvement of mean biases, compared to the regression retrieval which does not consider local features, suggests that the moving-window technique can produce better regression retrievals by including the local climatology in the regression model. For further improvement of the regression retrieval, one dimensional variational (1DVAR) physical model was also included in our algorithm. Error covariance matrix for the moving-window regression was obtained by using pre-developed regression retrieval and its error covariance. To assess the performance of 1DVAR using the mowing-window regression as a priori, error statistics of the physical retrievals from clear-sky AIRS measurements during four months of observation (March, June, September, and December of 2010) were comparedthe results obtained using new a priori information were compared with those using a priori information from a global set of training data which are classified into six classes of infrared (IR) window channel brightness temperature. This comparison demonstrated that the physical retrieval from the moving-window regression shows better result in terms of the root mean square error (RMSE) improvement. For temperature, RMSE improvements of 0.1 – 0.2 K and 0.25 – 0.5 K were achieved over the 150 – 300 hPa and 900 – 1000 hPa layers, respectively. For water vapor given as relative humidity, the RMSE was reduced by 1.5 – 3.5% above the 300 hPa level and by 0.5 – 1% within the 700 – 950 hPa layer. As most of improvements due to use of the moving-window technique were shown in situations in which the relationship between measured radiances and atmospheric state is not clear, we investigated a possible use of surface data for further improving AIRS temperature and humidity retrievals over the boundary layer. Surface data were statistically and physically used for our AIRS retrieval algorithm. Results showing reduced RMSEs at both the surface level and the boundary layer, suggest that the use of surface data can help better resolve vertical structure of temperature and moisture near the surface layer by alleviating the influences of incomplete channel weighting function near the surface on the retrieval. In conclusion, developing regionally focused algorithm, the inclusion of climate features in the AIRS retrieval algorithm can result in better temperature and humidity retrievals. Further improvement was also demonstrated by adding surface station data to the channel radiances as pseudo channels. Since the hyperspectral sounder is available on the geostationary platform, the development of regionally focused algorithm could enhance its applicability to enhance our ability to monitor and forecast severe weather.1. Introduction 1 2. Review of previous satellite-based temperature and humidity soundings 7 3. Infrared hyperspectral measurements 18 4. Development of regionally focused regression model 24 4.1. Construction of training data 24 4.2. Moving-window regression model 32 4.3. Detecting clear-sky FOVS from MODIS measurements 35 4.4. Error analysis 37 4.4.1. Validation by using independent simulation dataset 37 4.4.2. Case study 50 4.4.3. Comparison retrievals from real observation with reanalysis data 55 5. Impact of a priori information improvement on accuracy of 1DVAR 62 5.1. 1DVAR model 62 5.1.1. Background error covariance 63 5.1.2. Averaging kernel 68 5.1.3. Residual analysis for convergence criteria and quality control 68 5.2. Error analysis 74 5.2.1. Validation by using independent simulation dataset 74 5.2.2. Case study 83 5.2.3. Comparison retrievals from real observation with reanalysis data 87 6. Synergetic use of AWS data for AIRS T/q retrievals 94 6.1. Impact of AWS data on AIRS T/q soundings: Statistical perspective 98 6.1.1. Pseudo-AWS data for training 98 6.1.2. Retrieval sensitivity related to error of AWS data 101 6.1.3. Change of regression coefficient due to use of AWS data 108 6.1.4. Application 113 6.2. Impact of AWS data on AIRS T/q soundings: Physical perspective 115 6.2.1. 1DVAR with AWS observation 115 6.2.2. Result 117 7. Summary and discussion 120 References 125 국문초록 135Docto