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

THz spectroscopy of the atmosphere for climatology and meteorology applications

We present a new satellite-based instrument concept that will enable global measurements of atmospheric temperature and humidity profiles with unprecedented resolution and accuracy, compared to currently planned missions. It will also provide global measurements of essential climate variables related to ice clouds that will better constrain global climate models. The instrument is enabled by the use of superconducting detectors coupled to superconducting filterbank spectrometers, operating between 50GHz and 850 GHz. We present the science drivers, the current instrument concept and status, and predicted performance

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

Remote Measurements of Volcanic Gases Using Thermal Infrared Hyperspectral Imaging.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2018

How Well Can Infrared Sounders Observe the Atmosphere and Surface Through Clouds?

Infrared sounders, such as the Atmospheric Infrared Sounder (AIRS), the Infrared Atmospheric Sounding Interferometer (IASI), and the Cross-track Infrared sounder (CrIS), have a cloud-impenetrable disadvantage in observing the atmosphere and surface under opaque cloudy conditions. However, recent studies indicate that hyperspectral, infrared sounders have the ability to detect cloud effective-optical and microphysical properties and to penetrate optically thin clouds in observing the atmosphere and surface to a certain degree. We have developed a retrieval scheme dealing with atmospheric conditions with cloud presence. This scheme can be used to analyze the retrieval accuracy of atmospheric and surface parameters under clear and cloudy conditions. In this paper, we present the surface emissivity results derived from IASI global measurements under both clear and cloudy conditions. The accuracy of surface emissivity derived under cloudy conditions is statistically estimated in comparison with those derived under clear sky conditions. The retrieval error caused by the clouds is shown as a function of cloud optical depth, which helps us to understand how well infrared sounders can observe the atmosphere and surface through clouds

Combining satellite observations with a virtual ground-based remote sensing network for monitoring atmospheric stability

Atmospheric stability plays an essential role in the evolution of weather events. While the upper troposphere is sampled by satellite sensors, and in-situ sensors measure the atmospheric state close to the surface, only sporadic information from radiosondes or aircraft observations is available in the planetary boundary layer. Ground-based remote sensing offers the possibility to continuously and automatically monitor the atmospheric state in the boundary layer. Microwave radiometers (MWR) provide temporally resolved temperature and humidity profiles in the boundary layer and accurate values of integrated water vapor and liquid water path, while the DIfferential Absorption Lidar (DIAL) measures humidity profiles with high vertical and temporal resolution up to 3000 m height. Both instruments have the potential to complement satellite observations by additional information from the lowest atmospheric layers, particularly under cloudy conditions. The main objective of this work is to investigate the potential of ground-based and satellite sensors, as well as their synergy, for monitoring atmospheric stability. The first part of the study represents a neural network retrieval of stability indices, integrated water vapor, and liquid water path from simulated satellite- and ground-based measurements based on the reanalysis COSMO-REA2. The satellite-based instruments considered in the study are the currently operational Spinning Enhanced Visible and InfraRed Imager (SEVIRI) and the future Infrared Sounder (IRS), both in geostationary orbit, and the Advanced Microwave Sounding Unit (AMSU-A) and Infrared Atmospheric Sounding Interferometer (IASI), both deployed on polar orbiting satellites. Compared to the retrieval based on satellite observations, the additional ground-based MWR/DIAL measurements provide valuable improvements not only in the presence of clouds, which represent a limiting factor for infrared SEVIRI, IRS, and IASI, but also under clear sky conditions. The root-mean-square error for Convective Available Potential Energy (CAPE), for instance, is reduced by 24% if IRS observations are complemented by ground-based MWR measurements. The second part represents an attempt to assess the representativeness of observations of a single ground-based MWR and the impact of a network of MWR if combined with future geostationary IRS measurements. For this purpose, the reanalysis fields (150*150 km) in the western part of Germany were used to simulate MWR and IRS observations and to develop a neural network retrieval of CAPE and Lifted Index (LI). Further analysis was performed in the space of retrieved parameters CAPE and LI. The impact of additional ground-based network observations was investigated in two ways. First, using spatial statistical interpolation method, the fields of CAPE/LI retrieved from IRS observations were merged with the CAPE/LI values from MWR network taking into account the corresponding error covariance matrices of both retrievals. Within this method, the contribution of a ground-based network consisting of a varying number of radiometers (from one to 25) was shown to be significant under cloudy conditions. The second approach mimics the assimilation of satellite and ground-based observations in the space of retrieved CAPE/LI fields. Assuming the persistence of atmospheric fields for a period of six hours, the CAPE/LI fields calculated from reanalysis were taken as a first guess in an assimilation step. Observations, represented by CAPE/LI fields obtained from satellite and ground-based measurements with +6 hours delay, were assimilated by spatial interpolation. Within this method, the added value of ground-based observations, if compared to satellite contribution, is highly dependent on the current weather situation, cloudiness, and the position of ground-based instruments. For CAPE, the synergy of ground-based MWR and satellite IRS observations is essential even under clear sky conditions, since both passive sensors can not capture atmospheric profiles, needed for calculation of CAPE, with sufficient accuracy. Whereas for LI, the assimilation of observations of 25 MWR distributed in the domain is equivalent to the assimilation of horizontally resolved IRS observations, indicating that in the presence of clouds, MWR observations could replace cloud-affected IRS measurements. Within both approaches, it could be shown that the contribution of ground-based observations is more pronounced under cloudy conditions and is most valuable for the first 25 sensors located in the domain

Potential of EUMETSAT MTG-IRS hyperspectral sounder for improving nowcasting and very short range forecast atmospheric models

Obiettivo delle attività di ricerca descritte in questa tesi è lo studio dell’utilizzo dei dati iperspettrali IR per la diagnosi dell’instabilità atmosferica ed il rilevamento anticipato di sistemi convettivi. Lo studio è stato condotto nell’ambito del progetto MTG-IRS Near Real Time, concepito e coordinato da EUMETSAT per potenziare la preparazione degli utenti sulle potenzialità dello strumento IRS a supporto della meteorologia ed in particolare delle attività di previsioni a brevissima scadenza. In dettaglio, i prodotti iperspettrali di levello 2 di IRS, generati a partire da dati reali di IASI e CrIS e distribuiti da EUMETSAT, sono stati processati in quasi tempo reale insieme a dati ausiliari geograficamente co-localizzati ed indipendenti al fine di valutare la correlazione tra il segnale (cioè il contenuto informativo dei prodotti di livello 2) ed il fenomeno meteorologico (l’instabilità convettiva). Lo studio comprende anche il riprocessamento di una serie di casi di studio significativi sull’Italia. I risultati della ricerca mostrano che lo sfruttamento dei dati iperspettrali nel settore delle previsioni a brevissima scadenza è in grado di potenziare la capacità e la prontezza a livello utente dei moderni Servizi Meteorologici operativi per quanto riguarda il rilevamento in anticipo dei fenomeni intensi.In this thesis the research activities aiming at the investigation on the use of hyperspectral IR data for the diagnosis of atmospheric instability and the early detection of convective systems are shown. The study was carried out in the framework of MTG-IRS Near Real Time Demonstration Project, conceived and leaded by EUMETSAT to enhance the user awareness on the potential of the IRS instrument in support to the meteorology and in particular to the nowcasting activities. In detail, the proxy IRS hyperspectral level 2 products, generated from real IASI and CrIS data and distributed by EUMETSAT, were processed in near real time together with auxiliary colocated and independent datasets to assess the correlation between the signal (i.e. the information content of level 2 products) and the weather phenomenon (convective instability). The reprocess of a set of significant case studies over Italy was also included in the study. Research results show that the exploitation of hyperspectral data in the field of nowcasting applications could enhance the capacity and user-readiness of modern, operational Meteorological Services with respect to the early detection of severe weather

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