Surface radiation budget for climate applications

The Surface Radiation Budget (SRB) consists of the upwelling and downwelling radiation fluxes at the surface, separately determined for the broadband shortwave (SW) (0 to 5 micron) and longwave (LW) (greater than 5 microns) spectral regions plus certain key parameters that control these fluxes, specifically, SW albedo, LW emissivity, and surface temperature. The uses and requirements for SRB data, critical assessment of current capabilities for producing these data, and directions for future research are presented

The role of global cloud climatologies in validating numerical models

The purpose of this work is to estimate sampling errors of area-time averaged rain rate due to temporal samplings by satellites. In particular, the sampling errors of the proposed low inclination orbit satellite of the Tropical Rainfall Measuring Mission (TRMM) (35 deg inclination and 350 km altitude), one of the sun synchronous polar orbiting satellites of NOAA series (98.89 deg inclination and 833 km altitude), and two simultaneous sun synchronous polar orbiting satellites--assumed to carry a perfect passive microwave sensor for direct rainfall measurements--will be estimated. This estimate is done by performing a study of the satellite orbits and the autocovariance function of the area-averaged rain rate time series. A model based on an exponential fit of the autocovariance function is used for actual calculations. Varying visiting intervals and total coverage of averaging area on each visit by the satellites are taken into account in the model. The data are generated by a General Circulation Model (GCM). The model has a diurnal cycle and parameterized convective processes. A special run of the GCM was made at NASA/GSFC in which the rainfall and precipitable water fields were retained globally for every hour of the run for the whole year

Estimation of hourly land surface heat fluxes over the Tibetan Plateau by the combined use of geostationary and polar-orbiting satellites

Estimation of land surface heat fluxes is important for energy and water cycle studies, especially on the Tibetan Plateau (TP), where the topography is unique and the land–atmosphere interactions are strong. The land surface heating conditions also directly influence the movement of atmospheric circulation. However, high-temporal-resolution information on the plateau-scale land surface heat fluxes has been lacking for a long time, which significantly limits the understanding of diurnal variations in land–atmosphere interactions. Based on geostationary and polar-orbiting satellite data, the surface energy balance system (SEBS) was used in this paper to derive hourly land surface heat fluxes at a spatial resolution of 10&thinsp;km. Six stations scattered throughout the TP and equipped for flux tower measurements were used to perform a cross-validation. The results showed good agreement between the derived fluxes and in situ measurements through 3738 validation samples. The root-mean-square errors (RMSEs) for net radiation flux, sensible heat flux, latent heat flux and soil heat flux were 76.63, 60.29, 71.03 and 37.5&thinsp;W&thinsp;m−2, respectively; the derived results were also found to be superior to the Global Land Data Assimilation System (GLDAS) flux products (with RMSEs for the surface energy balance components of 114.32, 67.77, 75.6 and 40.05&thinsp;W&thinsp;m−2, respectively). The diurnal and seasonal cycles of the land surface energy balance components were clearly identified, and their spatial distribution was found to be consistent with the heterogeneous land surface conditions and the general hydrometeorological conditions of the TP.</p

A Comprehensive Analysis Of Clouds, Radiation, And Precipitation In The North Pacific Itcz In The NASA GISS Modele GCM And Satellite Observations

Global circulation/climate models (GCMs) remain as an invaluable tool to predict future potential climate change. To best advise policy makers, assessing and increasing the accuracy of climate models is paramount. The treatment of clouds, radiation and precipitation in climate models and their associated feedbacks have long been one of the largest sources of uncertainty in predicting any potential future climate changes. Three versions of the NASA GISS ModelE GCM (the frozen CMIP5 version [C5], a post-CMIP5 version with modifications to cumulus and boundary layer turbulence parameterizations [P5], and the most recent version of the GCM which builds on the post-CMIP5 version with further modifications to convective cloud ice and cold pool parameterizations [E5]) have been compared with various satellite observations to analyze how recent modifications to the GCM has impacted cloud, radiation, and precipitation properties. In addition to global comparisons, two areas are showcased in regional analyses: the Eastern Pacific Northern ITCZ (EP-ITCZ), and Indonesia and the Western Pacific (INDO-WP). Changes to the cumulus and boundary layer turbulence parameterizations in the P5 version of the GCM have improved cloud and radiation estimations in areas of descending motion, such as the Southern Mid-Latitudes. Ice particle size and fall speed modifications in the E5 version of the GCM have decreased ice cloud water contents and cloud fractions globally while increasing precipitable water vapor in the model. Comparisons of IWC profiles show that the GCM simulated IWCs increase with height and peak in the upper portions of the atmosphere, while 2C-ICE observations peak in the lower levels of the atmosphere and decrease with height, effectively opposite of each other. Profiles of CF peak at lower heights in the E5 simulation, which will potentially increase outgoing longwave radiation due to higher cloud top temperatures, which will counterbalance the decrease in reflected shortwave associated with lower CFs and the thinner optical depths associated with decreased IWC and LWC in the E5 simulation. Vertical motion within the newest E5 simulation is greatly weakened over the EP-ITCZ region, potentially due to atmospheric loading from enhanced ice particle fall speeds. Comparatively, E5 simulated upward motion in the INDO-WP is stronger than its predecessors. Changes in the E5 simulation have resulted in stronger/weaker upward motion over the ocean/land in the INDO-WP region in comparison with both the C5 and P5 predecessors. Multimodel precipitation analysis shows that most of the GCMs tend to produce a wider ITCZ with stronger precipitation compared to GPCP and TRMM precipitation products. E5-simulated precipitation decreases and shifts Southward over the Easter Pacific ITCZ, which warrants further investigation into meridional heat transport and radiation fields

Detection of vegetation drying signals using diurnal variation of land surface temperature: Application to the 2018 East Asia heatwave

Satellite-based vegetation monitoring provides important insights regarding spatiotemporal variations in vegetation growth from a regional to continental scale. Most current vegetation monitoring methodologies rely on spectral vegetation indices (VIs) observed by polar-orbiting satellites, which provide one or a few observations per day. This study proposes a new methodology based on diurnal changes in land surface temperatures (LSTs) using Japan's geostationary satellite, Himawari-8/Advanced Himawari Imager (AHI). AHI thermal infrared observation provides LSTs at 10-min frequencies and ∼ 2 km spatial resolution. The DTC parameters that summarize the diurnal cycle waveform were obtained by fitting a diurnal temperature cycle (DTC) model to the time-series LST information for each day. To clarify the applicability of DTC parameters in detecting vegetation drying under humid climates, DTC parameters from in situ LSTs observed at vegetation sites, as well as those from Himawari-8 LSTs, were evaluated for East Asia. Utilizing the record-breaking heat wave that occurred in East Asia in 2018 as a case study, the anomalies of DTC parameters from the Himawari-8 LSTs were compared with the drying signals indicated by VIs, latent heat fluxes (LE), and surface soil moisture (SM). The results of site-based and satellite-based analyses revealed that DTR (diurnal temperature range) correlates with the evaporative fraction (EF) and SM, whereas Tmax (daily maximum LST) correlates with LE and VIs. Regarding other temperature-related parameters, T0 (LST around sunrise), Ta (temperature rise during daytime), and δT (temperature fall during nighttime) are unstable in quantification by DTC model. Moreover, time-related parameters, such as tm (time reaching Tmax), are more sensitive to topographic slope and geometric conditions than surface thermal properties at humid sites in East Asia, although they correlate with EF and SM at a semi-arid site in Australia. Additionally, the spatial distribution of the DTR anomaly during the 2018 heat wave corresponds with the drying signals indicated as negative SM anomalies. Regions with large positive anomalies in Tmax and DTR correspond to area with visible damage to vegetation, as indicated by negative VI anomalies. Hence, combined Tmax and DTR potentially detects vegetation drying indetectable by VIs, thereby providing earlier and more detailed vegetation monitoring in both humid and semi-arid climates

A physics-constrained machine learning method for mapping gapless land surface temperature

More accurate, spatio-temporally, and physically consistent LST estimation has been a main interest in Earth system research. Developing physics-driven mechanism models and data-driven machine learning (ML) models are two major paradigms for gapless LST estimation, which have their respective advantages and disadvantages. In this paper, a physics-constrained ML model, which combines the strengths in the mechanism model and ML model, is proposed to generate gapless LST with physical meanings and high accuracy. The hybrid model employs ML as the primary architecture, under which the input variable physical constraints are incorporated to enhance the interpretability and extrapolation ability of the model. Specifically, the light gradient-boosting machine (LGBM) model, which uses only remote sensing data as input, serves as the pure ML model. Physical constraints (PCs) are coupled by further incorporating key Community Land Model (CLM) forcing data (cause) and CLM simulation data (effect) as inputs into the LGBM model. This integration forms the PC-LGBM model, which incorporates surface energy balance (SEB) constraints underlying the data in CLM-LST modeling within a biophysical framework. Compared with a pure physical method and pure ML methods, the PC-LGBM model improves the prediction accuracy and physical interpretability of LST. It also demonstrates a good extrapolation ability for the responses to extreme weather cases, suggesting that the PC-LGBM model enables not only empirical learning from data but also rationally derived from theory. The proposed method represents an innovative way to map accurate and physically interpretable gapless LST, and could provide insights to accelerate knowledge discovery in land surface processes and data mining in geographical parameter estimation