Estimating High Spatial Resolution Clear-Sky Land Surface Longwave Radiation Budget from MODIS and GOES Data

The surface radiation budget (SRB) is important in addressing a variety of scientific and application issues related to climate trends, hydrological and biogeophysical modeling, and agriculture. The three longwave components of SRB are surface downwelling, upwelling, and net longwave radiation (LWDN, LWUP, and LWNT). Existing surface longwave radiation budget (SLRB) datasets have coarse spatial resolution and their accuracy needs to be greatly improved. This study develops new hybrid methods for estimating instantaneous clear-sky high spatial resolution land LWDN and LWUP from the Moderate Resolution Imaging Spectroradiometer (MODIS, 1km) and the Geostationary Operational Environmental Satellites (GOES, 2-10 km) data. The hybrid methods combine extensive radiation transfer (physical) and statistical analysis (statistical) and share the same general framework. LWNT is derived from LWDN and LWUP. This study is the first effort to estimate SLRB using MODIS 1 km data. The new hybrid methods are unique in at least two other aspects. First, the radiation transfer simulation accounted for land surface emissivity effect. Second, the surface pressure effect in LWDN was considered explicitly by incorporating surface elevation in the statistical models. Nonlinear models were developed using the simulated databases to estimate LWDN from MODIS TOA radiance and surface elevation. Artificial Neural Network (ANN) models were developed to estimate LWUP from MODIS TOA radiance. The LWDN and LWUP models can explain more than 93.6% and 99.6% of variations in the simulated databases, respectively. Preliminary study indicates that similar hybrid methods can be developed to estimate LWDN and LWUP from the current GOES-12 Sounder data and the future GOES-R data. The new hybrid methods and alternative methods were evaluated using two years of ground measurements at six validation sites from the Surface Radiation Budget Network (SURFRAD). Validation results indicate the hybrid methods outperform alternative methods. The mean RMSEs of MODIS-derived LWDN, LWUP, and LWNT using the hybrid methods are 16.88, 15.23, and 17.30 W/m2. The RMSEs of GOES-12 Sounder-derived LWDN and LWUP are smaller than 23.70 W/m2. The high spatial resolution MODIS and GOES SLRB derived in this study is more accurate than existing datasets and can be used to support high resolution numerical models

A Feedforward Neural Network Approach for the Detection of Optically Thin Cirrus From IASI-NG

The identification of optically thin cirrus is crucial for their accurate parameterization in climate and Earth's system models. This study exploits the characteristics of the infrared atmospheric sounding interferometer-new generation (IASI-NG) to develop an algorithm for the detection of optically thin cirrus. IASI-NG has been designed for the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) polar system second-generation program to continue the service of its predecessor IASI from 2024 onward. A thin-cirrus detection algorithm (TCDA) is presented here, as developed for IASI-NG, but also in parallel for IASI to evaluate its performance on currently available real observations. TCDA uses a feedforward neural network (NN) approach to detect thin cirrus eventually misidentified as clear sky by a previously applied cloud detection algorithm. TCDA also estimates the uncertainty of "clear-sky" or "thin-cirrus" detection. NN is trained and tested on a dataset of IASI-NG (or IASI) simulations obtained by processing ECMWF 5-generation reanalysis (ERA5) data with the s-IASI radiative transfer model. TCDA validation against an independent simulated dataset provides a quantitative statistical assessment of the improvements brought by IASI-NG with respect to IASI. In fact, IASI-NG TCDA outperforms IASI TCDA by 3% in probability of detection (POD), 1% in bias, and 2% in accuracy, and the false alarm ratio (FAR) passes from 0.02 to 0.01. Moreover, IASI TCDA validation against state-of-the-art cloud products from Cloudsat/CPR and CALIPSO/Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) real observations reveals a tendency for IASI TCDA to underestimate the presence of thin cirrus (POD = 0.47) but with a low FAR (0.07), which drops to 0.0 for very thin cirrus

The Assimilation of Hyperspectral Satellite Radiances in Global Numerical Weather Prediction

Hyperspectral infrared radiance data present opportunities for significant improvements in data assimilation and Numerical Weather Prediction (NWP). The increase in spectral resolution available from the Atmospheric Infrared Sounder (AIRS) sensor, for example, will make it possible to improve the accuracy of temperature and moisture fields. Improved accuracy of the NWP analyses and forecasts should result. In this thesis we incorporate these hyperspectral data, using new assimilation methods, into the National Centers for Environmental Prediction's (NCEP) operational Global Data Assimilation System/Global Forecast System (GDAS/GFS) and investigate their impact on the weather analysis and forecasts. The spatial and spectral resolution of AIRS data used by NWP centers was initially based on theoretical calculations. Synthetic data were used to determine channel selection and spatial density for real time data assimilation. Several problems were previously not fully addressed. These areas include: cloud contamination, surface related issues, dust, and temperature inversions. In this study, several improvements were made to the methods used for assimilation. Spatial resolution was increased to examine every field of view, instead of one in nine or eighteen fields of view. Improved selection criteria were developed to find the best profile for assimilation from a larger sample. New cloud and inversion tests were used to help identify the best profiles to be assimilated in the analysis. The spectral resolution was also increased from 152 to 251 channels. The channels added were mainly near the surface, in the water vapor absorption band, and in the shortwave region. The GFS was run at or near operational resolution and contained all observations available to the operational system. For each experiment the operational version of the GFS was used during that time. The use of full spatial and enhanced spectral resolution data resulted in the first demonstration of significant impact of the AIRS data in both the Northern and Southern Hemisphere. Experiments were performed to show the contribution to the improvements in global weather forecasts from the increase in spatial and spectral resolution. Both spatial and spectral resolution increases were shown to make significant contributions to forecast skill. New methods were also developed to check for clouds, inversions and for estimating surface emissivity. Overall, an improved methodology for assimilating hyperspectral AIRS data was achieved

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

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

Using GOES-16 ABI data to detect convection, estimate latent heating, and initiate convection in a high resolution model

2021 Spring.Includes bibliographical references.Convective-scale data assimilation has received more attention in recent years as spatial resolution of forecast models has become finer and more observation data are available at such fine scale. Significant amounts of observation data are available over the globe, but only a limited number of observations are assimilated in operational forecast models in the most effective way. One of the most important observation data for predicting precipitation is radar reflectivity from ground-based radars as it provides three-dimensional structure of precipitation. Many operational models use these data to create cloud analysis and initiate convection. In High-Resolution Rapid Refresh (HRRR), the cloud permitting operational model at National Oceanic and Atmospheric Administration (NOAA) that is responsible for short term forecasts over the Contiguous United States (CONUS), latent heating is derived from ground-based radars and added in the observed convective regions to initiate convection. Even though adding heating is shown to improve forecasts of convection, this cannot be done over ocean or mountainous regions where radar data is not available. Geostationary data are available regardless of radar coverage and its data are provided in similar spatial and temporal resolution as ground-based radar. Currently, geostationary data are only used as a source of cloud top information or atmospheric motion vectors due to lack of vertical information. However, Geostationary Operational Environmental Satellites (GOES)-16 and -17 have high temporal resolution data that can compensate the lack of vertical information. From loops of one-minute visible images, convective clouds can be detected by finding a region with a constant bubbling. Therefore, this dissertation seeks a way to use these high temporal resolution GOES-16 data to mimic what radars do over land. In the first two papers presented in the dissertation, two methods are proposed to detect convection using one-minute GOES-16 Advanced Baseline Imager (ABI) data. The first method explicitly calculates Tb decrease or lumpiness of reflectance data and finds convective regions. The second paper tries to automate this process using machine learning method. Results from both methods are comparable to radar product, but the machine learning model seems to detect more convective regions than the conventional method. In the third paper, latent heating profiles for convective clouds are estimated from GOES-16. Once a convective cloud is detected, latent heating profiles corresponding to cloud top temperature of the convective cloud is searched from the lookup table created using model simulations. This technique is similar to spaceborne radar inferred latent heating developed for National Aeronautics and Space Administration (NASA)'s Global Precipitation Measurement Mission (GPM). Latent heating assigned from GOES-16 is shown to be similar to latent heating derived from Next-Generation Radar (NEXRAD) once they are summed up over each cloud. Finally in the last paper, latent heating estimated by using the method from the third paper are assimilated into the Weather Research and Forecasting (WRF) model to examine impacts of using GOES-16 derived latent heating in initiating convection in the forecast model. Two case studies are presented to compare results using GOES-16 derived heating and NEXRAD derived heating. Results show that using GOES-16 derived heating sometimes produce deeper convection than it should, but it improves overall precipitation forecasts. This appears related to the much deeper column of heating assigned by GOES than the empirical relation used by the HRRR operational scheme. In addition, in a case when storms developed over Gulf of Mexico where radar data are not available, forecasts are improved using GOES-16 latent heating

Remote Sensing Monitoring of Land Surface Temperature (LST)

This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research

Data Fusion and Synergy of Active and Passive Remote Sensing; An application for Freeze Thaw Detections

There has been a recent evolvement in the field of remote sensing after increase of number satellites and sensors data which could be fused to produce new data and products. These efforts are mainly focused on using of simultaneous observations from different platforms with different spatial and temporal resolutions. The research dissertation aims to enhance the synergy use of active and passive microwave observations and examine the results in detection land freeze and thaw (FT) predictions. Freeze thaw cycles particularly in high-latitude regions have a crucial role in many applications such as agriculture, biogeochemical transitions, hydrology and ecosystem studies. The dielectric change between frozen ice and melted water can dramatically affect the brightness temperature (TB) signal when water transits from the liquid to the solid phase which makes satellite-based microwave remote sensing unique for characterizing the surface freeze thaw status. Passive microwave (PMW) sensors with coarse resolution (about 25 km) but more frequent observations (at least twice a day and more frequent in polar regions) have been successfully utilized to define surface state in terms of freeze and thaw in the past. Alternatively, active microwave (AMW) sensors provide much higher spatial resolution (about 100 m or less) though with less temporal resolution (each 12 days). Therefore, an integration of microwave data coming from different sensors may provide a more complete estimation of land freeze thaw state. In this regard, the overarching goal of this research is to explore estimating high spatiotemporal freeze and thaw states using the combination of passive and active microwave observations. To obtain a high temporal resolution TB, this study primarily builds an improved diurnal variation of land surface temperature from integration of infrared sensors. In the next step, a half an hourly diurnal cycle of TB based on fusion of different passive sensors is estimated. It should be mentioned that each instrument has its own footprint, resolution, viewing angle, as well as frequency and consequently their data need to be harmonized in order to be combined. Later, data from an AMW sensor with fine spatial resolution are merged and compared to the corresponding passive data in order to find a relation between TB and backscatter data. Subsequently, PMW TB map can be downscaled to a higher spatial resolution or AMW backscatter timeseries can be generalized to high temporal resolution. Eventually, the final high spatiotemporal resolution TB product is used to examine the freeze thaw state for case studies areas in Northern latitudes