nasa-gesdisc/es-publications-researchareas huggingface dataset:
Stratospheric tropical warming event and its impact on the polar and tropical troposphere (2017)

Abstract. Stratospheretroposphere coupling is investigated in relation to middle atmospheric subtropical jet (MASTJ) variations in boreal winter. An exceptional strengthening of the MASTJ occurred in association with a sudden equatorward shift of the stratospheric polar night jet (PNJ) in early December 2011. This abrupt transformation of the MASTJ and PNJ had no apparent relation to the upward propagation of planetary waves from the troposphere. The impact of this stratospheric event penetrated into the troposphere in two regions: in the northern polar region and the tropics. Due to the strong MASTJ, planetary waves at higher latitudes were deflected and trapped in the northern polar region. Trapping of the planetary waves resulted in amplification of zonal wave number 1 component, which appeared in the troposphere as the development of a trough over the Atlantic sector and a ridge over the Eurasian sector. A strong MASTJ also suppressed the equatorward propagation of planetary waves, which resulted in weaker tropical stratospheric upwelling and produced anomalous warming in the tropical stratosphere. In the tropical tropopause layer (TTL), however, sublimation of ice clouds kept the temperature change minor. In the troposphere, an abrupt termination of a MaddenJulian Oscillation (MJO) event occurred following the static stability increase in the TTL. This termination suggests that the stratospheric event affected the convective episode in the troposphere.

Research areas: Atmospheric/Ocean Indicators
Satellite-based estimation and validation of monthly rainfall distribution over Upper Ganga river basin (2014)

Abstract. Water is one of the most precious natural resources for all living flora and fauna. 97.5% of water on the Earth is sea water, the remaining 2.5 % is fresh water of which slightly over two thirds is frozen in glaciers and polar ice caps. The unfrozen fresh water is mainly found as groundwater, with only a small fraction present above ground or in the air. Since one of the main source of water is rainfall. Therefore, proper information on rainfall and its variability in space and time is required for better watershed planning and management and other applications. In Himalayan basin, the rain gauge network is relatively sparse with uneven distribution. Hence, there is lack of proper information on rainfall patterns of this region. The main advantage of satellite derived rainfall estimation over rain gauge derived rain data is that these provide homogenous spatio-temporal rainfall information over a large area e.g. Upper Ganga river basin region. Therefore, a better understanding of the rainfall patterns of this region is required for better disaster mitigation. The objectives of this study are to evaluate the reliability of Tropical Rainfall Measuring Mission (TRMM) 3B43 V7 derived high resolution satellite product to study the rainfall distribution over the Upper Ganga river basin. TRMM 3B43 V7 derived monthly rainfall data is analyzed and the monthly rainfall product is validated and correlated with IMD (Indian Meteorological Department) gauge station's rainfall data. The monthly rainfall data of 15 years i.e. from 1998 to 2012 is used in the study. Statistical indices can be used to evaluate, compare and validate satellite rainfall data with respect to gauge rainfall data. Statistical indices used in this study are Correlation Coefficient (r), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Average Percentage Error (Avg. % Error). Most of the rainfall in the study area occurs in the months of June, July, August, September and October. The isohyets were prepared using gauge rainfall data and are matched with the spatially distributed rainfall surface prepared from TRMM satellite data for all the months of the rainy season of the study area. Kriging spatial interpolation method was used to generate the spatially distributed rainfall surface. From the results it was observe d that they matched fairly well with each other showing high spatial correlation. The monthly rainfall result showed that TRMM data is underestimated with low accuracy, though TRMM data and rain gauge data have positive correlation.

Research areas: Validation
Testing the skill of numerical hydraulic modeling to simulate spatiotemporal flooding patterns in the Logone floodplain, Cameroon (2016)

Recent innovations in hydraulic modeling have enabled global simulation of rivers, including simulation of their coupled wetlands and floodplains. Accurate simulations of floodplains using these approaches may imply tremendous advances in global hydrologic studies and in biogeochemical cycling. One such innovation is to explicitly treat sub-grid channels within two-dimensional models, given only remotely sensed data in areas with limited data availability. However, predicting inundated area in floodplains using a sub-grid model has not been rigorously validated. In this study, we applied the LISFLOOD-FP hydraulic model using a sub-grid channel parameterization to simulate inundation dynamics on the Logone River floodplain, in northern Cameroon, from 2001 to 2007. Our goal was to determine whether floodplain dynamics could be simulated with sufficient accuracy to understand human and natural contributions to current and future inundation patterns. Model inputs in this data-sparse region include in situ river discharge, satellite-derived rainfall, and the shuttle radar topography mission (SRTM) floodplain elevation. We found that the model accurately simulated total floodplain inundation, with a Pearson correlation coefficient greater than 0.9, and RMSE less than 700km2, compared to peak inundation greater than 6000km2. Predicted discharge downstream of the floodplain matched measurements (NashSutcliffe efficiency of 0.81), and indicated that net flow from the channel to the floodplain was modeled accurately. However, the spatial pattern of inundation was not well simulated, apparently due to uncertainties in SRTM elevations. We evaluated model results at 250, 500 and 1000-m spatial resolutions, and found that results are insensitive to spatial resolution. We also compared the model output against results from a run of LISFLOOD-FP in which the sub-grid channel parameterization was disabled, finding that the sub-grid parameterization simulated more realistic dynamics. These results suggest that analysis of global inundation is feasible using a sub-grid model, but that spatial patterns at sub-kilometer resolutions still need to be adequately predicted.

Research areas: Floods
Numerical simulation of an intense precipitation event over Rudraprayag in the central Himalayas during 1314 September 2012 (2015)

A recent heavy precipitation event on 13 September 2012 and the associated landslide on 14 September 2012 is one of the most severe calamities that occurred over the Rudraprayag region in Uttarakhand, India. This heavy precipitation event is also emblematic of the natural hazards occuring in the Himalayan region. Study objectives are to present dynamical fields associated with this event, and understand the processes related to the severe storm event, using the Weather Research and Forecasting (WRF ver 3.4) model. A triple-nested WRF model is configured over the Uttarakhand region centered over Ukhimath (3030N; 79 15E), where the heavy precipitation event is reported. Model simulation of the intense storm on 13 September 2012 is with parameterized and then with explicit convection are examined for the 3 km grid spacing domain. The event was better simulated without the consideration of convection parameterization for the innermost domain. The role of steep orography forcings is notable in rapid dynamical lifting as revealed by the positive vorticity and high reflectivity values and the intensification of the monsoonal storm. Incursion of moist air, in the lower levels, converges at the foothills of the mountains and rise along the orography to form the updraft zone of the storm. Such rapid unstable ascent leads to deep convection and increases the condensation rate of the water vapour forming clouds at a swift rate. This culminates into high intensity precipitation which leads to high amount of surface runoff over regions of susceptible geomorphology causing the landslide. Even for this intense and potentially unsual rainfall event, the processes involved appear to be the classic enhanced convective activity by orographic lifting of the moist air, as an important driver of the event.

Research areas: Atmospheric/Ocean Indicators, Land Surface/Agriculture Indicators
Evaluating factors responsible for contrasting signature of wasteland development in northern and southern Ganga Plains (Bihar State, India) with focus on waterlogging (2014)

The present study provides assessment of wasteland development in Bihar State based on satellite data. Wasteland covers 6.90 % (6,501 km2) of the state area and exhibits dominance of waterlogged areas in North Bihar (25.28 %) and scrubland (26.61 %) in South Bihar. The waterlogged areas in the state are dominantly associated with northern Bihar plains (94 %) with minor development (6 %) in southern Bihar plains. Such unequal distribution of waterlogged land areas in northern and southern Bihar plains is largely governed by high cumulative discharge generated in the large catchment area in Himalayan mountain ranges in the upland areas of northern Bihar plains in contrast to low cumulative discharge generated within small catchments in the Chota Nagpur Plateau in the southern Bihar plains. It is evaluated that the relief and groundwater level define the primary controlling factors, whereas rainfall, watershed area, and upland/plain ratio exert secondary control. Lower relief areas with high rainfall exhibit high soil moisture thereby inducing conditions of waterlogging. The study signifies the potential of satellite image-based evaluation of waterlogging through the use of Digital Elevation Model, Tropical Rainfall Measuring Mission-based rainfall measurement, and temporal waterlogging assessment together with other terrain parameters for conceptual understanding of waterlogging in northern Bihar plains.

Research areas: Habitat Conversion/Fragmentation, Land Surface/Agriculture Indicators
Conditions associated with large rain-field areas for tropical cyclones landfalling over Florida (2014)

To adequately warn the public about the potential for heavy rainfall, it is important to understand the conditions associated with large rain fields as tropical cyclones (TCs) move over land. This study examines rain-field areas during 29 landfalls over Florida utilizing data from the Tropical Rainfall Measuring Missions 3B42 product. A GIS was used to calculate the areas of regions delineated by 5 mm h1 rain rates occurring within 48 h pre- or post-landfall. Thirteen conditions were evaluated, including intensity, motion, vertical wind shear, cause of demise, month, and time of day. After dividing observations into groups for each condition, MannWhitney U and KruskalWallis tests determined that statistically significant differences existed among the groups for 11 conditions. The largest rain fields belonged to TCs that occurred in October or November, followed by those that were hurricanes and/or were intensifying at landfall. TCs nearing the end of an extratropical transition as well as those observed at 1100 Eastern Daylight Time also had relatively large areas of moderate-to-high rain rates. TCs meeting these conditions should be monitored closely as they move towards and over Florida due to their potential for widespread moderate-to-heavy rainfall, which may begin prior to the arrival of the fastest winds.

Research areas: Severe Storms
Use of the NASA Giovanni Data System for Geospatial Public Health Research: Example of Weather-Influenza Connection (2014)

The NASA Giovanni data analysis system has been recognized as a useful tool to access and analyze many different types of remote sensing data. The variety of environmental data types has allowed the use of Giovanni for different application areas, such as agriculture, hydrology, and air quality research. The use of Giovanni for researching connections between public health issues and Earths environment and climate, potentially exacerbated by anthropogenic influence, has been increasingly demonstrated. In this communication, the pertinence of several different data parameters to public health will be described. This communication also provides a case study of the use of remote sensing data from Giovanni in assessing the associations between seasonal influenza and meteorological parameters. In this study, logistic regression was employed with precipitation, temperature and specific humidity as predictors. Specific humidity was found to be associated (p < 0.05) with influenza activity in both temperate and tropical climate. In the two temperate locations studied, specific humidity was negatively correlated with influenza; conversely, in the three tropical locations, specific humidity was positively correlated with influenza. Influenza prediction using the regression models showed good agreement with the observed data (correlation coefficient of 0.50.83).

Research areas: Public Health
Tumbi Landslide, Papua New Guinea: rainfall induced? (2013)

On 24 January 2012, a fatal landslide with an estimated volume of 3 Mm3 hit villagers and infrastructure in the Tagali Valley, Southern Highlands Province, Papua New Guinea (PNG). Although a moderate event by PNG standards, the associated human casualties and infrastructure destruction give a human as well as a scientific need to review the potential causes for the event. PNG experiences numerous landslides annually, most of which are trigged either by rainfall events or seismic activity. Here, we review the short-term rainfall patterns leading up to the slope failure at Tumbi Quarry and compare the rainfall accumulations obtained over durations of 30, 60 and 90 days prior to the landslide, with comparable rainfall accumulations obtained for other failures observed in PNG over a 12-year period. Additionally, a review of long-term rainfall over a period of 6 months prior to the failure is completed as well as an assessment of seismic activity. Based on our analysis, we believe a seismic trigger to be very unlikely, while the increase in rainfall in the 2 weeks prior to the failure in conjunction with a high-intensity rainfall event at the end of October 2011 could have had a greater influence in enhancing rather than restraining slope failure. This, in addition to natural denudational processes, geological structure and the anthropogenic activity in the vicinity of the landslide, could all have served to affect slope stability.

Research areas: Land Surface/Agriculture Indicators
Remote Sensing Analysis of Lake Dynamics in Semi-Arid Regions: Implication for Water Resource Management. Lake Manyara, East African Rift, Northern Tanzania (2013)

We show here that a remote sensing (RS) approach is a cost-efficient and accurate method to study water resource dynamics in semi-arid areas. We use a MODIS surface reflectance dataset and a Modified Normalized Difference Water Index (MNDWI) to map the variability of Lake Manyaras water surface area using a histogram segmentation technique. The results indicate that Lake Manyaras water surface coverage has been decreasing from 520.25 km2 to 30.5 km2 in 2000 and 2011 respectively. We observe that the lake water surface and the lake water balance displayed a similar pattern from 2006 to 2009, probably initiated by heavy rainfall and low temperature in 2006. Lake water surface area appears to have an inverse relationship with MODIS evapotranspiration (ET) and MODIS land surface temperature (LST). We imply that recent fluctuations of Lake Manyaras surface water area are a direct consequence of global and regional climate fluctuations. We therefore conclude that, by means of RS it is possible to provide timely and up-to-date water resource information to managers and hence enable optimized and operational decisions for sustainable management and conservation. We suggest that the method employed in this research should be applied to monitor water resource dynamics provided that remotely sensed datasets are available.

Research areas: Land Surface/Agriculture Indicators, Atmospheric/Ocean Indicators
Near real-time disturbance detection using satellite image time series (2012)

Near real-time monitoring of ecosystem disturbances is critical for rapidly assessing and addressing impacts on carbon dynamics, biodiversity, and socio-ecological processes. Satellite remote sensing enables cost-effective and accurate monitoring at frequent time steps over large areas. Yet, generic methods to detect disturbances within newly captured satellite images are lacking. We propose a multi-purpose time-series-based disturbance detection approach that identifies and models stable historical variation to enable change detection within newly acquired data. Satellite image time series of vegetation greenness provide a global record of terrestrial vegetation productivity over the past decades. Here, we assess and demonstrate the method by applying it to (1) simulated time series of vegetation greenness data from satellite data, (2) real-world satellite greenness image time series between February 2000 and July 2011 covering Somalia to detect drought-related vegetation disturbances. First, simulation results illustrate that disturbances are successfully detected in near real-time while being robust to seasonality and noise. Second, major drought-related disturbance corresponding with most drought-stressed regions in Somalia are detected from mid-2010 onwards. The method can analyse in-situ or satellite data time series of biophysical indicators from local to global scale since it is fast, does not depend on thresholds and does not require time series gap filling. While the data and methods used are appropriate for proof-of-concept development of global scale disturbance monitoring, specific applications (e.g., drought or deforestation monitoring) mandate integration within an operational monitoring framework (e.g., http://www.fews.net/).

Research areas: Land Surface/Agriculture Indicators
Validation of Satellite Precipitation Adjustment Methodology From Seven Basins in the Continental United States1 (2012)

Tobin, Kenneth J. and Marvin E. Bennett, 2012. Validation of Satellite Precipitation Adjustment Methodology From Seven Basins in the Continental United States. Journal of the American Water Resources Association (JAWRA) 48(2): 221-234. DOI: 10.1111/j.1752-1688.2011.00604.x Abstract: The precipitation science community has expressed concern regarding the ability of satellite-based precipitation products to accurately capture rainfall values over land. There has been some work that has focused on addressing the deficiencies of satellite precipitation products, particularly on the adjustment of bias. This article outlines a methodology that adjusts satellite products utilizing ground-based precipitation data. The approach is not a simple bias adjustment, but is a three-step process that transforms a satellite product based on a ground-based precipitation product (NEXRAD-derived Multisensor Precipitation Estimator [MPE] product or rain-gauge data). The developed methodology was successfully applied to seven moderate-to-large sized watersheds from continental United States (CONUS) and northern Mexico over a spectrum of climatic regimes ranging from dry to humid settings. Methodology validation is based on comparison of observed and simulated streamflow generated with SWAT (Soil and Water Assessment Tool) model using unadjusted and adjusted precipitation products as input. Streamflow comparison is based on mass balance error and Nash-Sutcliffe efficiency coefficient. Finally, the contribution of how adjustment to correct misses, false alarms, and bias impacts adjusted datasets and the potential impact that the adjustment methodology can have on hydrological applications such as water resource monitoring and flood prediction are explored.

Research areas: Validation
Intra-seasonal variability in Oceansat-2 scatterometer sea-surface winds over the Indian summer monsoon region (2012)

In September 2009, the Indian Space Research Organisation launched a Ku-band microwave scatterometer (OSCAT) onboard the polar orbiting satellite Oceansat-2. In this article, the capabilities of the newly available OSCAT sea-surface winds are demonstrated by studying the monsoon intra-seasonal variabilities during the 2010 summer monsoon season. A preliminary validation of OSCAT surface winds with European Centre for Medium Range Weather Forecasting (ECMWF) analysis surface winds carried out during June to August 2010 suggests that the quality of the OSCAT winds are able to meet the mission specifications. The observed mean monthly features of the Indian summer monsoon in July and August 2010 from OSCAT match well with those of ECMWF reanalysis winds. The OSCAT winds capture the known characteristics of the Indian summer monsoon, such as the northward propagation of a low level jet, and its preferred locations during active and break monsoon conditions, reasonably well. The Morlet wavelet transform is used for time series analysis. The OSCAT measured sea-surface winds were found to possess two dominant modes of variability during the 2010 monsoon season: one with a periodicity between 32 and 64 days, and another with a periodicity between 8 and 16 days. Rainfall activity over the Indian summer monsoon region is closely associated with the phases of the two above-mentioned dominant intra-seasonal variabilities. This study demonstrates that the OSCAT winds can be used very well and with confidence for meteorological studies.

Research areas: Validation