ALBERT

Description: This study investigates the efficiency of the major operational global ensemble forecast systems of the world in capturing the spatiotemporal evolution of the forecast uncertainty. Using data from 2015, it updates the results of an earlier study based on data from 2012. It also tests, for the first time on operational ensemble data, two quantitative relationships to aid in the interpretation of the raw ensemble forecasts. One of these relationships provides a flow-dependent prediction of the reliability of the ensemble in capturing the uncertain forecast features, while the other predicts the 95th percentile value of the magnitude of the forecast error. It is found that, except for the system of the Met Office, the main characteristics of the ensemble forecast systems have changed little between 2012 and 2015. The performance of the UKMO ensemble improved in predicting the overall magnitude of the uncertainty, but its ability to predict the dominant uncertain forecast features was degraded. A common serious limitation of the ensemble systems remains that they all have major difficulties with predicting the large-scale atmospheric flow in the long (longer than 10 days) forecast range. These difficulties are due to the inability of the ensemble members to maintain large-scale waves in the forecasts, which presents a stumbling block in the way of extending the skill of numerical weather forecasts to the subseasonal range. The two tested predictive relationships were found to provide highly accurate predictions of the flow-dependent reliability of the ensemble predictions and the 95th percentile value of the magnitude of the forecast error for the operational ensemble forecast systems.

Description: The National Climate Assessment-Land Data Assimilation System (NCA-LDAS) is an Integrated Terrestrial Water Analysis, and is one of NASAs contributions to the NCA of the United States. The NCA-LDAS has undergone extensive development, including multi-variate assimilation of remotely-sensed water states and anomalies as well as evaluation and verification studies, led by the Goddard Space Flight Centers Hydrological Sciences Laboratory (HSL). The resulting NCA-LDAS data have recently been released to the general public and include those from the Noah land-surface model (LSM) version 3.3 (Noah-3.3) and the Catchment LSM version Fortuna-2.5 (CLSM-F2.5). Standard LSM output variables including soil moistures temperatures, surface fluxes, snow cover depth, groundwater, and runoff are provided, as well as streamflow using a river routing system. The NCA-LDAS data are archived at and distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). The data can be accessed via HTTP, OPeNDAP, Mirador search and download, and NASA Earth data Search. To further facilitate access and use, the NCA-LDAS data are integrated into the NASA Giovanni, for quick visualization and analysis, and into the Data Rods system, for retrieval of time series of long time periods. The temporal and spatial resolutions of the NCA-LDAS data are, respectively, daily-averages and 0.125x0.125 degree, covering North America (25N 53N; 125W 67W) and the period January 1979 to December 2015. The data files are in self-describing, machine-independent, CF-compliant netCDF-4 format.

Description: NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is one of twelveNASA Earth Observing System (EOS) data centers that process, archive, document, and distributedata from Earth science missions and related projects. The GES DISC hosts a wide range ofremotely-sensed and model data and provides reliable and robust data access and services to usersworldwide. This presentation, focusing on hydrological land surface data, provides a summary tablefor the hydrological data holdings, along with discussions of recent updates to data and data services.

Description: Social media data streams are important sources of real-time and historical global information for science applications. At the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), we are exploring the Twitter data stream for its potential in augmenting the validation program of NASA Earth science missions, specifically the Global Precipitation Measurement (GPM) mission. We have implemented a tweet processing infrastructure that outputs classified precipitation tweets. Inputs are "passive" tweets, along with a smaller number of tweets from "active" participants, i.e., those knowingly contributing to our effort. The "active" tweets, presumably of higher quality, enrich the Twitter stream. "Active" sources include data scraped from other social media (e.g., public Facebook posts) and data from existing crowdsourcing programs (e.g., mPING reports). In addition, there is likely relevant precipitation information in images and documents that are the end points of links often included in tweets. Information derived from these "active" sources could then be tweeted into the Twitter stream, thus enriching its quality. The objective of our current work is to mine these tweet linked images and documents, using neural networks, to increase the information content and quality related to precipitation. For images, we classified them as either precipitation-related or not. For training and validation, we used images obtained via the Google custom search API. We created two models: (1) by training a simple Convolutional Neural Network and (2) by using transfer learning principles to adapt a pre-trained object recognition model. For documents, both those linked to tweets and the tweet contents, we trained Hierarchical Attention Networks to determine precipitation occurrence, type, and intensity. For training and validation, we used a keyword-filtered tweet data set labelled with ground truth data from Dark Sky (an API to retrieve weather-related labels) and the National Severe Storms Laboratory's Multi Radar/Multi-Sensor (MRMS) system. Our results demonstrated the efficacy of our machine learning approaches for enriching the Twitter stream, to derive information potentially useful for validation of earth science satellite data.

Description: The Gravity Recovery and Climate Experiment (GRACE) mission detects changes in Earth's gravity field by precisely monitoring the changes in distance between two satellites orbiting the Earth in tandem. Scientists at NASA's Goddard Space Flight Center generate GRACE-assimilated groundwater and soil moisture drought indicators each week, for drought monitor-related studies and applications. The GRACE-assimilated Drought Indicator Version 2.0 data product (GRACE-DA-DM V2.0) is archived at, and distributed by, the NASA GES DISC (Goddard Earth Sciences Data and Information Services Center). More information about the data and data access is available on the data product landing page at https://disc.gsfc.nasa.gov/datasets /GRACEDADM_CLSM0125US_7D_2.0/summary. The GRACE-DA-DM V2.0 data product contains three drought indicators: Groundwater Percentile, Root Zone Soil Moisture Percentile, and Surface Soil Moisture Percentile. The drought indicators are of wet or dry conditions, expressed as a percentile, indicating the probability of occurrence within the period of record from 1948 to 2012. These GRACE-assimilated drought indicators, with improved spatial and temporal resolutions, should provide a more comprehensive and objective identification of drought conditions. This presentation describes the basic characteristics of the data and data services at NASA GES DISC and collaborative organizations, and uses a few examples to demonstrate the simple ways to explore the GRACE-assimilated drought indicator data.

Description: The Twitter data stream is an important new source of real-time and historical global information for potentially augmenting the validation program of NASA's Global Precipitation Measurement (GPM) mission. There have been other similar uses of Twitter, though mostly related to natural hazards monitoring and management. The validation of satellite precipitation estimates is challenging, because many regions lack data or access to data, especially outside of the U.S. and in remote and developing areas. The time-varying set of "precipitation" tweets can be thought of as an organic network of rain gauges, potentially providing a widespread view of precipitation occurrence. Twitter provides a large source of crowd for crowdsourcing. During a 24-hour period in the middle of the snow storm this past March in the U.S. Northeast, we collected more than 13,000 relevant precipitation tweets with exact geolocation. The overall objective of our project is to determine the extent to which processed tweets can provide additional information that improves the validation of GPM data. Though our current effort focuses on tweets and precipitation, our approach is general and applicable to other social media and other geophysical measurements. Specifically, we have developed an operational infrastructure for processing tweets, in a format suitable for analysis with GPM data; engaged with potential participants, both passive and active, to "enrich" the Twitter stream; and inter-compared "precipitation" tweet data, ground station data, and GPM retrievals. In this presentation, we detail the technical capabilities of our tweet processing infrastructure, including data abstraction, feature extraction, search engine, context-awareness, real-time processing, and high volume (big) data processing; various means for "enriching" the Twitter stream; and results of inter-comparisons. Our project should bring a new kind of visibility to Twitter and engender a new kind of appreciation of the value of Twitter by the science research communities.

Description: Extreme weather and climate events, such as heavy rainfall, heatwave, floods and droughts, and strong wind, can have devastating impacts on society. NASA and NOAA, based on independent analyses, recently announced that global surface temperatures in 2018 are the fourth warmest since 1880, behind only those of 2016, 2017, and 2015 (nasa.gov). Also in 2018, the United States experienced 14 billion-dollar disasters, ranking as the fourth highest total number of such events, behind only the years 2017, 2011, and 2016 (climate.gov). Many research studies have focused on acquiring observational and modeling data, to reveal linkages between increasing extreme events, global water and energy cycle, and global climate change. However, draw conclusions is still a challenge. NASA Goddard Earth Sciences Data and Information Services Center is one of twelve NASA Earth Observing System (EOS) data centers that process, archive, document, and distribute data from Earth science missions and related projects. The GES DISC hosts a wide range of remotely-sensed and model data and provides reliable and robust data access and services to users worldwide. This presentation provides a few examples of extreme event study that use Land Surface Model (LSM) assimilated, quality-controlled, and spatially and temporally consistent, hydrological data from the GES DISC. Also provided is a summary table for the hydrological data holdings, along with discussions of recent updates to data and data services.

Description: Recently, the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) has released global land 3-hourly Potential Evapotranspiration and Supporting Forcing Data Version-1 (PET_PU_3H025.001), at 0.25x0.25 degree spatial resolution, spanning the 23 year period from 1984 to 2006. The Version-2 will be released in the near future, covering longer time period. This dataset was generated by Professor Justin Sheffield through NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) project. Potential evapotranspiration (PET) is a representation of the environmental demand for evapotranspiration (ET). ET and PET are important part of the global water cycle estimation, and are also critical to advance our understanding of the climate system. NASA GES DISC archives and distributes various global and regional ET datasets from several projects, for example, Land Data Assimilation System (LDAS), Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), other MEaSUREs Projects, such as Land Surface Atmospheric Boundary Interaction Product by William Rossow; and SRB/GEWEX evapotranspiration (Penman-Monteith) by Eric F. Wood. In this presentation, we will overview all available PET and ET datasets and services at GES DISC. As examples, climatology and some seasonal characteristics of PET and selected ET will be shown. The data can be accessed from NASA GES DISC (https://disc.gsfc.nasa.gov/) by searching keyword "evapotranspiration".

Description: Earth Science data are available in many file formats (NetCDF, HDF, GRB, etc.) and in a wide range of sizes, from kilobytes to gigabytes. These properties have become a challenge to users if they are not familiar with these formats or only want a small region of interest (ROI) from a specific dataset. At NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), we have developed and implemented a multipurpose subset service to ease user access to Earth Science data. Our Level 3 & 4 Regridder is capable of subsetting across multiple parameters (spatially, temporally, by level, and by variable) as well as having additional beneficial features (temporal means, regridding to target grids, and file conversion to other data formats). In this presentation, we will demonstrate how users can use this service to better access only the data they need in the form they require.