Half a century of satellite remote sensing of sea-surface temperature

Sea-surface temperature (SST) was one of the first ocean variables to be studied from earth observation satellites. Pioneering images from infrared scanning radiometers revealed the complexity of the surface temperature fields, but these were derived from radiance measurements at orbital heights and included the effects of the intervening atmosphere. Corrections for the effects of the atmosphere to make quantitative estimates of the SST became possible when radiometers with multiple infrared channels were deployed in 1979. At the same time, imaging microwave radiometers with SST capabilities were also flown. Since then, SST has been derived from infrared and microwave radiometers on polar orbiting satellites and from infrared radiometers on geostationary spacecraft. As the performances of satellite radiometers and SST retrieval algorithms improved, accurate, global, high resolution, frequently sampled SST fields became fundamental to many research and operational activities. Here we provide an overview of the physics of the derivation of SST and the history of the development of satellite instruments over half a century. As demonstrated accuracies increased, they stimulated scientific research into the oceans, the coupled ocean-atmosphere system and the climate. We provide brief overviews of the development of some applications, including the feasibility of generating Climate Data Records. We summarize the important role of the Group for High Resolution SST (GHRSST) in providing a forum for scientists and operational practitioners to discuss problems and results, and to help coordinate activities world-wide, including alignment of data formatting and protocols and research. The challenges of burgeoning data volumes, data distribution and analysis have benefited from simultaneous progress in computing power, high capacity storage, and communications over the Internet, so we summarize the development and current capabilities of data archives. We conclude with an outlook of developments anticipated in the next decade or so

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

Radiation-Based Analytic Approaches to Investigate the Earth’s Atmosphere

Radiation, propagating through Earth’s atmosphere, plays an important role in the Earth system. Solar radiation is the major source of energy, followed by thermal infrared radiation emitted by the Earth. The total radiative energy budget affects dynamic, thermodynamics, photochemical and biological processes. In addition, by measuring the reflected and emitted radiation at a distance (e.g., satellite or aircraft), we can detect and monitor the physical characteristics of a region which can help researchers get a better understanding of Earth’s atmosphere. Therefore, radiation-based analytic approaches are powerful tools in Earth Science. This thesis focuses on using radiation-based analytic tools to study the Earth’s atmosphere and to understand human impacts on the Earth system. First, we develop novel machine learning methods for hyperspectral radiative transfer simulations. Hyperspectral technique is one of the most popular and powerful methods for atmospheric remote sensing and is widely used for temperature, gas, aerosol, and cloud retrievals. However, accurate forward radiative transfer simulations are computationally expensive since they require a larger number of monochromatic radiative transfer calculations. We, therefore explore the feasibility of machine learning techniques for fast hyperspectral radiative transfer simulations that perform calculations at a small fraction of hyperspectral wavelengths and extend them across the entire spectral range. The machine learning-based approach achieves better performance than the traditional principal component analysis (PCA) method. Second, we evaluate modeled hyperspectral infrared spectra against satellite all-sky observations. The national weather centers obtain data from hyperspectral infrared sounders on a global scale. The cloudless scenario of this data is used to initialize weather forecasts, including temperature, water vapor, water cloud, and ice cloud profiles on a global grid. Although the data from these satellites are sensitive to the vertical distribution of ice and liquid water in the clouds, this information is not fully utilized. In this study, we evaluate how well the modeled spectra compare to AIRS observations using different cloud overlap models. We hope that this information can be used to verify clouds in the National Meteorological Center model and to initialize forecasts in the future. In the last chapter, we use radiation-based analytic approaches to study human impacts on the Earth system. In the first study case, we show that the radiative forcing due to geospatially redistributed anthropogenic aerosols mainly determined the spatial variations of winter extreme weather in the Northern Hemisphere during 1970-2005, which is a unique transition period for global aerosol forcing. In the second case, we review satellite and ground-based observations and conduct state-of-art atmospheric model simulations during the COVID-19 lockdown period. The halted human activities during the COVID-19 pandemic in China provided a unique experiment to assess the efficiency of air-pollution mitigation.</p

Estimating Analysis Temperature And Humidity Biases Due To Assimilation Of Aerosol & Cloud Contaminated Hyperspectral Infrared Radiances

Observations from hyperspectral infrared sounder (HIS) instruments aboard earth-observing satellites have become a cornerstone of numerical weather prediction assimilation efforts – providing the largest decrease in forecast error of any assimilated satellite observations. The assimilation of infrared (IR) radiances is predicated on the assumption of clear-sky observations. Thus, any signal imparted upon the HIS radiances due to cloud or aerosol will likely result in unexpected and uncharacterized biases in analyzed temperature and humidity fields. Forecasts based upon these biased fields may have large inherent inaccuracies. The process of cloud and aerosol screening of passive satellite products and radiances is imperfect. Residual aerosol and cirrus clouds are found to contaminate HIS radiances assimilated from presumed clear-sky scenes at concerning rates (approximately 30% and 8% for the Naval Research Laboratory Variational Data Assimilation System, respectively). As such, the presence of an uncharacterized bias exists within model analyses. To determine the biases a modified one-dimensional variational (1DVar) assimilation system is used for two studies: one for aerosol, one for cloud. For the aerosol study, observations of dust from the Island of Tenerife, Spain are used to create synthetic dust contaminated HIS observations. For the cloud study, a series of clouds of varying optical depth and cloud top altitude are simulated. Analysis biases greater than expected forecast uncertainties are found for both studies. Aerosol biases are smaller, likely due to lower thermal contrast with the lower atmosphere. For instance, at an average aerosol optical depth of 0.30 a peak temperature bias of 0.5 K and dew point bias of 1.0 K is found. Meanwhile, for cloud optical depths as small as 0.1, maximum temperature and dew point biases of 3 K and 10 K are shown. Finally, a third study in similar vein to the first two simplifies the impact of aerosols on numerical weather prediction by examining the impact of aerosol optical model on broadband radiative properties. Observations above and within a dust aerosol plume collected during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) field campaign are used to attempt radiative closure. Large variability for different commonly used aerosol optical models is shown for shortwave fluxes and heating rates of up to 50% and 400%, respectively. In the IR, variability is still relatively smaller, but still very large at 3% for flux and 25-50% for heating rates. Finally, it is determined that aerosol analyses from models are not sufficiently accurate to provide accurate fluxes or heating rates

Applications of Satellite Earth Observations section - NEODAAS: Providing satellite data for efficient research

The NERC Earth Observation Data Acquisition and Analysis Service (NEODAAS) provides a central point of Earth Observation (EO) satellite data access and expertise for UK researchers. The service is tailored to individual users’ requirements to ensure that researchers can focus effort on their science, rather than struggling with correct use of unfamiliar satellite data

Satellite monitoring of harmful algal blooms (HABs) to protect the aquaculture industry

Harmful algal blooms (HABs) can cause sudden and considerable losses to fish farms, for example 500,000 salmon during one bloom in Shetland, and also present a threat to human health. Early warning allows the industry to take protective measures. PML's satellite monitoring of HABs is now funded by the Scottish aquaculture industry. The service involves processing EO ocean colour data from NASA and ESA in near-real time, and applying novel techniques for discriminating certain harmful blooms from harmless algae. Within the AQUA-USERS project we are extending this capability to further HAB species within several European countries

A review of high impact weather for aviation meteorology

This review paper summarizes current knowledge available for aviation operations related to meteorology and provides suggestions for necessary improvements in the measurement and prediction of weather-related parameters, new physical methods for numerical weather predictions (NWP), and next-generation integrated systems. Severe weather can disrupt aviation operations on the ground or in-flight. The most important parameters related to aviation meteorology are wind and turbulence, fog visibility, aerosol/ash loading, ceiling, rain and snow amount and rates, icing, ice microphysical parameters, convection and precipitation intensity, microbursts, hail, and lightning. Measurements of these parameters are functions of sensor response times and measurement thresholds in extreme weather conditions. In addition to these, airport environments can also play an important role leading to intensification of extreme weather conditions or high impact weather events, e.g., anthropogenic ice fog. To observe meteorological parameters, new remote sensing platforms, namely wind LIDAR, sodars, radars, and geostationary satellites, and in situ instruments at the surface and in the atmosphere, as well as aircraft and Unmanned Aerial Vehicles mounted sensors, are becoming more common. At smaller time and space scales (e.g., < 1 km), meteorological forecasts from NWP models need to be continuously improved for accurate physical parameterizations. Aviation weather forecasts also need to be developed to provide detailed information that represents both deterministic and statistical approaches. In this review, we present available resources and issues for aviation meteorology and evaluate them for required improvements related to measurements, nowcasting, forecasting, and climate change, and emphasize future challenges

Remote Sensing of Precipitation: Volume 2

Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne

Satellite and in situ observations for advancing global Earth surface modelling: a review

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort