Jason A. Otkin

A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing. 1. Model formulation

[1]xa0Due to the influence of evaporation on land-surface temperature, thermal remote sensing data provide valuable information regarding the surface moisture status. The Atmosphere-Land Exchange Inverse (ALEXI) model uses the morning surface temperature rise, as measured from a geostationary satellite platform, to deduce surface energy and water fluxes at 5–10 km resolution over the continental United States. Recent improvements to the ALEXI model are described. Like most thermal remote sensing models, ALEXI is constrained to work under clear-sky conditions when the surface is visible to the satellite sensor, often leaving large gaps in the model output record. An algorithm for estimating fluxes during cloudy intervals is presented, defining a moisture stress function relating the fraction of potential evapotranspiration obtained from the model on clear days to estimates of the available water fraction in the soil surface layer and root zone. On cloudy days, this stress function is inverted to predict the soil and canopy fluxes. The method is evaluated using flux measurements representative at the watershed scale acquired in central Iowa with a dense flux tower network during the Soil Moisture Experiment of 2002 (SMEX02). The gap-filling algorithm reproduces observed fluxes with reasonable accuracy, yielding ∼20% errors in ET at the hourly timescale, and 15% errors at daily timesteps. In addition, modeled soil moisture shows reasonable response to major precipitation events. This algorithm is generic enough that it can easily be applied to other thermal energy balance models. With gap-filling, the ALEXI model can estimate hourly surface fluxes at every grid cell in the U.S. modeling domain in near real-time. A companion paper presents a climatological evaluation of ALEXI-derived evapotranspiration and moisture stress fields for the years 2002–2004.

A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 2. Surface moisture climatology

[1]xa0Robust satellite-derived moisture stress indices will be beneficial to operational drought monitoring, both in the United States and globally. Using thermal infrared imagery from the Geostationary Operational Environmental Satellites (GOES) and vegetation information from the Moderate Resolution Imaging Spectrometer (MODIS), a fully automated inverse model of Atmosphere-Land Exchange (ALEXI) has been used to model daily evapotranspiration and surface moisture stress over a 10-km resolution grid covering the continental United States. Examining monthly clear-sky composites for April–October 2002–2004, the ALEXI evaporative stress index (ESI) shows good spatial and temporal correlation with the Palmer drought index but at considerably higher spatial resolution. The ESI also compares well to anomalies in monthly precipitation fields, demonstrating that surface moisture has an identifiable thermal signature that can be detected from space, even under dense vegetation cover. Simple empirical thermal drought indices like the vegetation health index do not account for important forcings on surface temperature, such as available energy and atmospheric conditions, and can therefore generate spurious drought detections under certain circumstances. Surface energy balance inherently incorporates these forcings, constraining ESI response in both energy- and water-limited situations. The surface flux modeling techniques described here have demonstrated skill in identifying areas subject to soil moisture stress on the basis of the thermal land surface signature, without requiring information regarding antecedent rainfall. ALEXI therefore may have potential for operational drought monitoring in countries lacking well-established precipitation measurement networks.

Objective Satellite-Based Detection of Overshooting Tops Using Infrared Window Channel Brightness Temperature Gradients

Abstract Deep convective storms with overshooting tops (OTs) are capable of producing hazardous weather conditions such as aviation turbulence, frequent lightning, heavy rainfall, large hail, damaging wind, and tornadoes. This paper presents a new objective infrared-only satellite OT detection method called infrared window (IRW)-texture. This method uses a combination of 1) infrared window channel brightness temperature (BT) gradients, 2) an NWP tropopause temperature forecast, and 3) OT size and BT criteria defined through analysis of 450 thunderstorm events within 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) imagery. Qualitative validation of the IRW-texture and the well-documented water vapor (WV) minus IRW BT difference (BTD) technique is performed using visible channel imagery, CloudSat Cloud Profiling Radar, and/or Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) cloud-top height for selected cases. Quantit...

Comparison of WRF Model-Simulated and MODIS-Derived Cloud Data

In this study, the ability of different combinations of bulk cloud microphysics and planetary boundary layer (PBL) parameterization schemes implemented in the Weather Research and Forecasting Model to realistically simulate the wide variety of cloud types associated with an extratropical cyclone is examined. An ensemble of high-resolution model simulations was constructed for this case using four microphysics and two PBL schemes characterized by different levels of complexity. Simulated cloud properties, including cloud optical thickness, cloud water path, cloud-top pressure, and radiative cloud phase, were subsequently compared to cloud data from three Moderate Resolution Imaging Spectroradiometer (MODIS) overpasses across different portions of the domain. A detailed comparison of the simulated datasets revealed that the PBL and cloud microphysics schemes both exerted a strong influence on the spatial distribution and physical properties of the simulated cloud fields. In particular, the low-level cloud properties were found to be very sensitive to the PBL scheme while the upper-level clouds were sensitive to both the microphysics and PBL schemes. Overall, the simulated cloud properties were broadly similar to the MODIS observations, with the most realistic cloud fields produced by the more sophisticated parameterization schemes.

An Intercomparison of Drought Indicators Based on Thermal Remote Sensing and NLDAS-2 Simulations with U.S. Drought Monitor Classifications

AbstractComparison of multiple hydrologic indicators, derived from independent data sources and modeling approaches, may improve confidence in signals of emerging drought, particularly during periods of rapid onset. This paper compares the evaporative stress index (ESI)—a diagnostic fast-response indicator describing evapotranspiration (ET) deficits derived within a thermal remote sensing energy balance framework—with prognostic estimates of soil moisture (SM), ET, and runoff anomalies generated with the North American Land Data Assimilation System (NLDAS). Widely used empirical indices based on thermal remote sensing [vegetation health index (VHI)] and precipitation percentiles [standardized precipitation index (SPI)] were also included to assess relative performance. Spatial and temporal correlations computed between indices over the contiguous United States were compared with historical drought classifications recorded in the U.S. Drought Monitor (USDM). Based on correlation results, improved forms for...

Validation of GOES-Based Insolation Estimates Using Data from the U.S. Climate Reference Network

Reliable procedures that accurately map surface insolation over large domains at high spatial and temporal resolution are a great benefit for making the predictions of potential and actual evapotranspiration that are required by a variety of hydrological and agricultural applications. Here, estimates of hourly and daily integrated insolation at 20-km resolution, based on Geostationary Operational Environmental Satellite (GOES) visible imagery are compared to pyranometer measurements made at 11 sites in the U.S. Climate Reference Network (USCRN) over a continuous 15-month period. Such a comprehensive survey is necessary in order to examine the accuracy of the satellite insolation estimates over a diverse range of seasons and land surface types. The relatively simple physical model of insolation that is tested here yields good results, with seasonally averaged model errors of 62 (19%) and 15 (10% )Wm 2 for hourly and daily-averaged insolation, respectively, including both clear- and cloudy-sky conditions. This level of accuracy is comparable, or superior, to results that have been obtained with more complex models of atmospheric radiative transfer. Model performance can be improved in the future by addressing a small elevation-related bias in the physical model, which is likely the result of inaccurate model precipitable water inputs or cloud-height assessments.

Examining Rapid Onset Drought Development Using the Thermal Infrared–Based Evaporative Stress Index

AbstractReliable indicators of rapid drought onset can help to improve the effectiveness of drought early warning systems. In this study, the evaporative stress index (ESI), which uses remotely sensed thermal infrared imagery to estimate evapotranspiration (ET), is compared to drought classifications in the U.S. Drought Monitor (USDM) and standard precipitation-based drought indicators for several cases of rapid drought development that have occurred across the United States in recent years. Analysis of meteorological time series from the North American Regional Reanalysis indicates that these events are typically characterized by warm air temperature and low cloud cover anomalies, often with high winds and dewpoint depressions that serve to hasten evaporative depletion of soil moisture reserves. Standardized change anomalies depicting the rate at which various multiweek ESI composites changed over different time intervals are computed to more easily identify areas experiencing rapid changes in ET. Overal...

Evaluating the Performance of Planetary Boundary Layer and Cloud Microphysical Parameterization Schemes in Convection-Permitting Ensemble Forecasts Using Synthetic GOES-13 Satellite Observations

AbstractIn this study, the ability of several cloud microphysical and planetary boundary layer parameterization schemes to accurately simulate cloud characteristics within 4-km grid-spacing ensemble forecasts over the contiguous United States was evaluated through comparison of synthetic Geostationary Operational Environmental Satellite (GOES) infrared brightness temperatures with observations. Four double-moment microphysics schemes and five planetary boundary layer (PBL) schemes were evaluated. Large differences were found in the simulated cloud cover, especially in the upper troposphere, when using different microphysics schemes. Overall, the results revealed that the Milbrandt–Yau and Morrison microphysics schemes tended to produce too much upper-level cloud cover, whereas the Thompson and the Weather Research and Forecasting Model (WRF) double-moment 6-class (WDM6) microphysics schemes did not contain enough high clouds. Smaller differences occurred in the cloud fields when using different PBL scheme...

Validation of a Large-Scale Simulated Brightness Temperature Dataset Using SEVIRI Satellite Observations

Abstract In this study, the accuracy of a simulated infrared brightness temperature dataset derived from a unique large-scale, high-resolution Weather Research and Forecasting (WRF) Model simulation is evaluated through a comparison with Spinning Enhanced Visible and Infrared Imager (SEVIRI) observations. Overall, the analysis revealed that the simulated brightness temperatures realistically depict many of the observed features, although several large discrepancies were also identified. The similar shapes of the simulated and observed probability distributions calculated for each infrared band indicate that the model simulation realistically depicted the cloud morphology and relative proportion of clear and cloudy pixels. A traditional error analysis showed that the largest model errors occurred over central Africa because of a general mismatch in the locations of deep tropical convection and intervening regions of clear skies and low-level cloud cover. A detailed inspection of instantaneous brightness te...

Examining the Relationship between Drought Development and Rapid Changes in the Evaporative Stress Index

AbstractIn this study, the ability of a new drought metric based on thermal infrared remote sensing imagery to provide early warning of an elevated risk for drought intensification is assessed. This new metric, called the rapid change index (RCI), is designed to highlight areas undergoing rapid changes in moisture stress as inferred from weekly changes in the evaporative stress index (ESI) generated using the Atmosphere–Land Exchange Inverse (ALEXI) surface energy balance model. Two case study analyses across the central United States revealed that the initial appearance of negative RCI values indicative of rapid increases in moisture stress preceded the introduction of severe-to-exceptional drought in the U.S. Drought Monitor (USDM) by more than 4 weeks. Using data from 2000 to 2012, the probability of USDM intensification of at least one, two, or three categories over different time periods was computed as a function of the RCI magnitude. Compared to baseline probabilities, the RCI-derived probabilities...