The Use of Visible Geostationary Operational Meteorological Satellite Imagery in Mapping the Water Balance over Puerto Rico for Water Resource Management

A solar insolation satellite remote sensing product for Puerto Rico, the US Virgin Islands (USVI), Dominican Republic, Haiti, Jamaica, and Cuba became available in 2009 through a collaboration between the University of Puerto Rico-Mayagüez Campus and the University of Alabama in Huntsville. Solar insolation data are available at 1 km resolution for Puerto Rico and the USVI and 2 km resolution for the other islands, as derived from 500 m resolution GOES-16 visible imagery. The insolation data demonstrate the powerful utility of satellite-derived fields for water resource applications, specifically the routine production of potential and reference evapotranspiration. This chapter describes the theoretical background and technical approach for estimating components of the daily water and energy balance in Puerto Rico. Useful information can be obtained from the model, which benefits disaster and emergency management, agriculture, human health, ecology, coastal water management, and renewable energy development at the island scale

Evaluation of Drought Indices Based on Thermal Remote Sensing of Evapotranspiration over the Continental United States

The reliability of standard meteorological drought indices based on measurements of precipitation is limited by the spatial distribution and quality of currently available rainfall data. Furthermore, they reflect only one component of the surface hydrologic cycle, and they cannot readily capture nonprecipitation-based moisture inputs to the land surface system (e.g., irrigation) that may temper drought impacts or variable rates of water consumption across a landscape. This study assesses the value of a new drought index based on remote sensing of evapotranspiration (ET). The evaporative stress index (ESI) quantifies anomalies in the ratio of actual to potential ET (PET), mapped using thermal band imagery from geostationary satellites. The study investigates the behavior and response time scales of the ESI through a retrospective comparison with the standardized precipitation indices and Palmer drought index suite, and with drought classifications recorded in the U.S. Drought Monitor for the 2000–09 growing seasons. Spatial and temporal correlation analyses suggest that the ESI performs similarly to short-term (up to 6 months) precipitation-based indices but can be produced at higher spatial resolution and without requiring any precipitation data. Unique behavior is observed in the ESI in regions where the evaporative flux is enhanced by moisture sources decoupled from local rainfall: for example, in areas of intense irrigation or shallow water table. Normalization by PET serves to isolate the ET signal component responding to soil moisture variability from variations due to the radiation load. This study suggests that the ESI is a useful complement to the current suite of drought indicators, with particular added value in parts of the world where rainfall data are sparse or unreliable

Towards Improved Forecasts of Atmospheric and Oceanic Circulations over the Complex Terrain of the Eastern Mediterranean

Forecasting atmospheric and oceanic circulations accurately over the Eastern Mediterranean has proved to be an exceptional challenge. The existence of fine-scale topographic variability (land/sea coverage) and seasonal dynamics variations can create strong spatial gradients in temperature, wind and other state variables, which numerical models may have difficulty capturing. The Hellenic Center for Marine Research (HCMR) is one of the main operational centers for wave forecasting in the eastern Mediterranean. Currently, HCMR's operational numerical weather/ocean prediction model is based on the coupled Eta/Princeton Ocean Model (POM). Since 1999, HCMR has also operated the POSEIDON floating buoys as a means of state-of-the-art, real-time observations of several oceanic and surface atmospheric variables. This study attempts a first assessment at improving both atmospheric and oceanic prediction by initializing a regional Numerical Weather Prediction (NWP) model with high-resolution sea surface temperatures (SST) from remotely sensed platforms in order to capture the small-scale characteristics

Thermal-Based Evaporative Stress Index for Monitoring Surface Moisture Depletion

The standard suite of indicators currently used in operational drought monitoring reflects anomalous conditions in several major components of the hydrologic budget—representing deficits in precipitation, soil moisture content, runoff, surface and groundwater storage, snowpack, and streamflow. In principle, it is useful to have a diversity of indices because drought can assume many forms (meteorological, agricultural, hydrological, and socioeconomic), over broad ranges in timescale (weeks to years), and with varied impacts of interest to different stakeholder groups. Farmers, for example, may be principally interested in soil moisture deficits, river forecasters will focus on streamflow fluctuations, and water managers will be concerned with longer-term stability in municipal water supply and reservoir levels. Only recently has actual evapotranspiration (ET) been considered as a primary indicator of drought conditions (e.g., Anderson et al., 2007b; Labedzki and Kanecka- Geszke, 2009; Li et al., 2005; Mo et al., 2010). ET is a valuable drought indicator because it reflects not only moisture availability but also the rate at which water is being consumed. Because transpiration (T) and carbon uptake by vegetation are tightly coupled through stomatal exchange, ET anomalies are indicative of vegetation health and growing conditions. In addition, the importance of so-called flash droughts is becoming increasingly evident, where hot, dry, and windy atmospheric conditions can lead to unusually rapid soil moisture depletion and, in some cases, devastating crop failure. Such events cannot be easily identified using local precipitation anomalies but should have a detectable ET signature

An Initial Assessment of a SMAP Soil Moisture Disaggregation Scheme Using TIR Surface Evaporation Data over the Continental United States

The Soil Moisture Active Passive (SMAP) mission is dedicated toward global soil moisture mapping. Typically, an L-band microwave radiometer has spatial resolution on the order of 36-40 km, which is too coarse for many specific hydro-meteorological and agricultural applications. With the failure of the SMAP active radar within three months of becoming operational, an intermediate (9-km) and finer (3-km) scale soil moisture product solely from the SMAP mission is no longer possible. Therefore, the focus of this study is a disaggregation of the 36-km resolution SMAP passive-only surface soil moisture (SSM) using the Soil Evaporative Efficiency (SEE) approach to spatial scales of 3-km and 9-km. The SEE was computed using thermal-infrared (TIR) estimation of surface evaporation over Continental U.S. (CONUS). The disaggregation results were compared with the 3 months of SMAP-Active (SMAP-A) and Active/Passive (AP) products, while comparisons with SMAP-Enhanced (SMAP-E), SMAP-Passive (SMAP-P), as well as with more than 180 Soil Climate Analysis Network (SCAN) stations across CONUS were performed for a 19 month period. At the 9-km spatial scale, the TIR-Downscaled data correlated strongly with the SMAP-E SSM both spatially (r = 0.90) and temporally (r = 0.87). In comparison with SCAN observations, overall correlations of 0.49 and 0.47; bias of 0.022 and 0.019 and unbiased RMSD of 0.105 and 0.100 were found for SMAP-E and TIR-Downscaled SSM across the Continental U.S., respectively. At 3-km scale, TIR-Downscaled and SMAP-A had a mean temporal correlation of only 0.27. In terms of gain statistics, the highest percentage of SCAN sites with positive gains (>55%) was observed with the TIR-Downscaled SSM at 9-km. Overall, the TIR-based downscaled SSM showed strong correspondence with SMAP-E; compared to SCAN, and overall both SMAP-E and TIR-Downscaled performed similarly, however, gain statistics show that TIR-Downscaled SSM slightly outperformed SMAP-E

A Study on Assimilation of CYGNSS Wind Speed Data for Tropical Convection during 2018 January MJO

The National Aeronautics and Space Administration (NASA) Cyclone Global Navigation Satellite System (CYGNSS) mission was launched in December 2016. CYGNSS provides ocean surface wind speed retrieval along specular reflection tracks at an interval resolution of approximately 25 km. With a median revisit time of 2.8 h covering a ±35° latitude, CYGNSS can provide more frequent and accurate measurements of surface wind over the tropical oceans under heavy precipitation, especially within tropical cyclone cores and deep convection regions, than traditional scatterometers. In this study, CYGNSS v2.1 Level 2 wind speed data were assimilated into the Weather Research and Forecasting (WRF) model using the WRF Data Assimilation (WRFDA) system with hybrid 3- and 4-dimensional variational ensemble technology. Case studies were conducted to examine the impact of the CYGNSS data on forecasts of tropical cyclone (TC) Irving and a westerly wind burst (WWB) during the Madden–Julian oscillation (MJO) event over the Indian Ocean in early January 2018. The results indicate a positive impact of the CYGNSS data on the wind field. However, the impact from the CYGNSS data decreases rapidly within 4 h after data assimilation. Also, the influence of CYGNSS data only on precipitation forecast is found to be limited. The assimilation of CYGNSS data was further explored with an additional experiment in which CYGNSS data was combined with Global Precipitation Mission (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) hourly precipitation and Advanced Scatterometer (ASCAT) wind vector and were assimilated into the WRF model. A significant positive impact was found on the tropical cyclone intensity and track forecasts. The short-term forecast of wind and precipitation fields were also improved for both TC Irving and the WWB event when the combined satellite data was assimilated

The Definition of GOES Infrared Lightning Initiation Interest Fields

The article of record as published may be located at http://dx.doi.org/10.1175/2010JAMC2575.1Within cumulus cloud fields that develop in conditionally unstable air masses, only a fraction of the cumuli may eventually develop into deep convection. Identifying which of these convective clouds is most likely to generate lightning often starts with little more than a qualitative visual satellite analysis. The goal of this study is to identify the observed satellite infrared (IR) signatures associated with growing cumulus clouds prior to the first lightning strike, or lightning initiation (LI). This study quantifies the behavior of 10 Geostationary Operational Environmental Satellite-12 (GOES-12) IR fields of interest in the 1 h in advance of LI. A total of 172 lightning-producing storms, which occurred during the 2009 convective season, are manually tracked and studied over four regions: northern Alabama, central Oklahoma, the Kennedy Space Center, and Washington, D.C. Four-dimensional and cloud-to-ground lightning array data provide a total cloud lightning picture (in-cloud, cloud-to-cloud, cloud-to-air, and cloud-to-ground) and thus precise LI points for each storm in both time and space. Statistical significance tests are conducted on observed trends for each of the 10 LI fields to determine the unique information each field provides in terms of behavior prior to LI. Eight out of 10 LI fields exhibited useful information at least 15 min in advance of LI, with 35 min being the average. Statistical tests on these eight fields are compared for separate large geographical areas. Median IR temperatures and 3.9-mm reflectance values are then determined for all 172 events as an outcome, which may be valuable when implementing a LI prediction algorithm into real-time satellite-based systems

Thermal-Based Evaporative Stress Index for Monitoring Surface Moisture Depletion

The standard suite of indicators currently used in operational drought monitoring reflects anomalous conditions in several major components of the hydrologic budget—representing deficits in precipitation, soil moisture content, runoff, surface and groundwater storage, snowpack, and streamflow. In principle, it is useful to have a diversity of indices because drought can assume many forms (meteorological, agricultural, hydrological, and socioeconomic), over broad ranges in timescale (weeks to years), and with varied impacts of interest to different stakeholder groups. Farmers, for example, may be principally interested in soil moisture deficits, river forecasters will focus on streamflow fluctuations, and water managers will be concerned with longer-term stability in municipal water supply and reservoir levels. Only recently has actual evapotranspiration (ET) been considered as a primary indicator of drought conditions (e.g., Anderson et al., 2007b; Labedzki and Kanecka- Geszke, 2009; Li et al., 2005; Mo et al., 2010). ET is a valuable drought indicator because it reflects not only moisture availability but also the rate at which water is being consumed. Because transpiration (T) and carbon uptake by vegetation are tightly coupled through stomatal exchange, ET anomalies are indicative of vegetation health and growing conditions. In addition, the importance of so-called flash droughts is becoming increasingly evident, where hot, dry, and windy atmospheric conditions can lead to unusually rapid soil moisture depletion and, in some cases, devastating crop failure. Such events cannot be easily identified using local precipitation anomalies but should have a detectable ET signature