Retrieval validation during the European Aqua Thermodynamic Experiment

Atmospheric and surface thermodynamic parameters retrieved with advanced hyperspectral remote sensors aboard Earth observing satellites are critical to weather prediction and scientific research. The retrieval algorithms and retrieved parameters from satellite sounders must be validated to demonstrate the capability and accuracy of both observation and data processing systems. The European Aqua Thermodynamic Experiment (EAQUATE) was conducted not only for validation of the Atmospheric InfraRed Sounder on the Aqua satellite, but also for assessment of validation systems of both ground-based and aircraft-based instruments that will be used for other satellite systems, such as the Infrared Atmospheric Sounding Interferometer on the European MetOp satellite, the Cross-track Infrared Sounder from the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project and the continuing series of NPOESS satellites. Detailed intercomparisons were conducted and presented using different retrieval methodologies: measurements from airborne ultraspectral Fourier transform spectrometers, aircraft in situ instruments, dedicated dropsondes and radiosondes, ground-based Raman lidar, as well as the European Centre for Medium-range Weather Forecasting modelled thermal structures. The results of this study not only illustrate the quality of the measurements and retrieval products, but also demonstrate the capability of the validation systems put in place to validate current and future hyperspectral sounding instruments and their scientific products

A 20-YEAR CLIMATOLOGY OF GLOBAL ATMOSPHERIC METHANE FROM HYPERSPECTRAL THERMAL INFRARED SOUNDERS WITH SOME APPLICATIONS

Atmospheric Methane (CH4) is the second most important greenhouse gas after carbon dioxide (CO2), and accounts for approximately 20% of the global warming produced by all well-mixed greenhouse gases. Thus, its spatiotemporal distributions and relevant long-term trends are critical to understanding the sources, sinks, and global budget of atmospheric composition, as well as the associated climate impacts. The current suite of hyperspectral thermal infrared sounders has provided continuous global methane data records since 2002, starting with the Atmospheric Infrared Sounder (AIRS) onboard the NASA EOS/Aqua satellite launched on 2 May 2002. The Cross-track Infrared Sounder (CrIS) was launched onboard the Suomi National Polar Orbiting Partnership (SNPP) on 28 October 2011 and then on NOAA-20 on 18 November 2017. The Infrared Atmospheric Sounding Interferometer (IASI) was launched onboard the EUMETSAT MetOp-A on 19 October 2006, followed by MetOp-B on 17 September 2012, then Metop-C on 7 November 2018. In this study, nearly two decades of global CH4 concentrations retrieved from the AIRS and CrIS sensors were analyzed. Results indicate that the global mid-upper tropospheric CH4 concentrations (centered around 400 hPa) increased significantly from 2003 to 2020, i.e., with an annual average of ~1754 ppbv in 2003 and ~1839 ppbv in 2020. The total increase is approximately 85 ppbv representing a +4.8% change in 18 years. More importantly, the rate of increase was derived using satellite measurements and shown to be consistent with the rate of increase previously reported only from in-situ observational measurements. It further confirmed that there was a steady increase starting in 2007 that became stronger since 2014, as also reported from the in-situ observations. In addition, comparisons of the methane retrieved from the AIRS and CrIS against in situ measurements from NOAA Global Monitoring Laboratory (GML) were conducted. One of the key findings of this comparative study is that there are phase shifts in the seasonal cycles between satellite thermal infrared measurements and ground measurements, especially in the middle to high latitudes in the northern hemisphere. Through this, an issue common in the hyperspectral thermal sensor retrievals were discovered that was unknown previously and offered potential solutions. We also conducted research on some applications of the retrieval products in monitoring the changes of CH4 over the selected regions (the Arctic and South America). Detailed analyses based on local geographic changes related to CH4 concentration increases were discussed. The results of this study concluded that while the atmospheric CH4 concentration over the Arctic region has been increasing since the early 2000s, there were no catastrophic sudden jumps during the period of 2008-2012, as indicated by the earlier studies using pre-validated retrieval products. From our study of CH4 climatology using hyperspectral infrared sounders, it has been proved that the CH4 from hyperspectral sounders provide valuable information on CH4 for the mid-upper troposphere and lower stratosphere. Future approaches are suggested that include: 1) Utilizing extended data records for CH4 monitoring using AIRS, CrIS, and other potential new generation hyperspectral infrared sensors; 2). Improving the algorithms for trace gas retrievals; and 3). Enhancing the capacity to detect CH4 changes and anomalies with radiance signals from hyperspectral infrared sounders

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

High-Accuracy Measurements of Total Column Water Vapor From the Orbiting Carbon Observatory-2

Accurate knowledge of the distribution of water vapor in Earth's atmosphere is of critical importance to both weather and climate studies. Here we report on measurements of total column water vapor (TCWV) from hyperspectral observations of near-infrared reflected sunlight over land and ocean surfaces from the Orbiting Carbon Observatory-2 (OCO-2). These measurements are an ancillary product of the retrieval algorithm used to measure atmospheric carbon dioxide concentrations, with information coming from three highly resolved spectral bands. Comparisons to high-accuracy validation data, including ground-based GPS and microwave radiometer data, demonstrate that OCO-2 TCWV measurements have maximum root-mean-square deviations of 0.9-1.3mm. Our results indicate that OCO-2 is the first space-based sensor to accurately and precisely measure the two most important greenhouse gases, water vapor and carbon dioxide, at high spatial resolution [1.3 x 2.3 km(exp. 2)] and that OCO-2 TCWV measurements may be useful in improving numerical weather predictions and reanalysis products

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

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

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

A systematic assessment of water vapor products in the Arctic: from instantaneous measurements to monthly means

Water vapor is an important component in the water and energy cycle of the Arctic. Especially in light of Arctic amplification, changes in water vapor are of high interest but are difficult to observe due to the data sparsity of the region. The ACLOUD/PASCAL campaigns performed in May/June 2017 in the Arctic North Atlantic sector offers the opportunity to investigate the quality of various satellite and reanalysis products. Compared to reference measurements at R/V Polarstern frozen into the ice (around 82∘ N, 10∘ E) and at Ny-Ålesund, the integrated water vapor (IWV) from Infrared Atmospheric Sounding Interferometer (IASI) L2PPFv6 shows the best performance among all satellite products. Using all radiosonde stations within the region indicates some differences that might relate to different radiosonde types used. Atmospheric river events can cause rapid IWV changes by more than a factor of 2 in the Arctic. Despite the relatively dense sampling by polar-orbiting satellites, daily means can deviate by up to 50 % due to strong spatio-temporal IWV variability. For monthly mean values, this weather-induced variability cancels out, but systematic differences dominate, which particularly appear over different surface types, e.g., ocean and sea ice. In the data-sparse central Arctic north of 84∘ N, strong differences of 30 % in IWV monthly means between satellite products occur in the month of June, which likely result from the difficulties in considering the complex and changing surface characteristics of the melting ice within the retrieval algorithms. There is hope that the detailed surface characterization performed as part of the recently finished Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) will foster the improvement of future retrieval algorithms