Method for Adaptive Labels on Content Based on the Accessibility Persona or Domain Expertise of the User

The present disclosure describes computer-implemented systems and methods for providing adaptable accessibility labels to web content and web applications by selecting labels that are targeted to different cohorts of users based on the user’s skill level and disability. Accessibility labels that are targeted to the user’s skill level and disability are selected from multiple variants of labels and provided to the user to help the user understand the application better than with the general labels that apply to all users regardless of skill level or disability

JACIE: A Model Partnership

The National Aeronautics and Space Administration (NASA), National Oceanic and Atmospheric Administration (NOAA), the United States Department of Agriculture (USDA), and the United States Geological Survey (USGS), and their associates and partners, are directly responsible for establishing and leading a unique interagency team of scientists and engineers who work together to evaluate and enhance the quality remote sensing data for commercial and government use. This team is called "the Joint Agency Commercial Imagery Evaluation (JACIE) team". The team works together to define, prioritize, assign, and assess civil and commercial image quality and jointly sponsors an annual JACIE Civil Commercial Imagery Evaluation workshop with participation support from the remote sensing calibration and validation science community

Comparative Analysis on Applicability of Satellite and Meteorological Data for Prediction of Malaria in Endemic Area in Bangladesh

Relationships between yearly malaria incidence and (1) climate data from weather station and (2) satellite-based vegetation health (VH) indices were investigated for prediction of malaria vector activities in Bangladesh. Correlation analysis of percent of malaria cases with Advanced Very High Resolution Radiometer- (AVHRR-) based VH indices represented by the vegetation condition index (VCI—moisture condition) and the temperature condition index (TCI—estimates thermal condition) and with rainfall, relative humidity, and temperature from ground-based meteorological stations. Results show that climate data from weather stations are poorly correlated and are not applicable to estimate prevalence in Bangladesh. The study also has shown that AVHRR-based vegetation health (VH) indices are highly applicable for malaria trend assessment and also for the estimation of the total number of malaria cases in Bangladesh for the period of 1992–2001

Comparative Analysis on Applicability of Satellite and Meteorological Data for Prediction of Malaria in Endemic Area in Bangladesh

Relationships between yearly malaria incidence and (1) climate data from weather station and (2) satellite-based vegetation health (VH) indices were investigated for prediction of malaria vector activities in Bangladesh. Correlation analysis of percent of malaria cases with Advanced Very High Resolution Radiometer- (AVHRR-) based VH indices represented by the vegetation condition index (VCI—moisture condition) and the temperature condition index (TCI—estimates thermal condition) and with rainfall, relative humidity, and temperature from ground-based meteorological stations. Results show that climate data from weather stations are poorly correlated and are not applicable to estimate prevalence in Bangladesh. The study also has shown that AVHRR-based vegetation health (VH) indices are highly applicable for malaria trend assessment and also for the estimation of the total number of malaria cases in Bangladesh for the period of 1992–2001

Why Operational Meteorologists Need More Satellite Soundings

No abstract availabl

The Unusual Evolution of Hurricane Arthur 2014

Hurricane Arthur (2014) was an early season hurricane that had its roots in a convective complex in the Southern Plains of the U.S. As the complex moved into northern Texas, a Mesoscale Convective Vortex (MCV) formed and drifted towards the east of the southern U.S. for a few days before emerging over the southwest Atlantic near South Carolina. The MCV drifted south and slowly acquired tropical characteristics, eventually becoming a Category 2 hurricane that would affect much of eastern North Carolina prior to the 4th of July holiday weekend. Arthur continued up the coast, brushing portions of southeast New England and merged with an upper-level low, completing a full tropical to extratropical-transition in the process, producing damaging wind gusts in portions of the Canadian Maritimes. As part of the GOES-R and JPSS Satellite Proving Grounds, multiple proxy and operational products were available to analyze and forecast this complex evolution. The Storm Prediction Center had products available to monitor the initial severe thunderstorm aspect, while the National Hurricane Center and Ocean Prediction Center were able to monitor the tropical and extratropical transition of Arthur using various convective and red, green, blue (RGB) products that have been introduced in recent years. This paper will discuss Arthur's evolution through the eyes of the various Satellite Proving Ground demonstrations

Second SNPP Cal/Val Campaign: Environmental Data Retrieval Analysis

Satellite ultraspectral infrared sensors provide key data records essential for weather forecasting and climate change science. The Suomi National Polar-orbiting Partnership (Soumi NPP) satellite Environmental Data Records (EDRs) are retrieved from calibrated ultraspectral radiance or Sensor Data Records (SDRs). Understanding the accuracy of retrieved EDRs is critical. The second Suomi NPP Calibration/Validation field campaign was conducted during March 2015 with flights over Greenland. The NASA high-altitude ER-2 aircraft carrying ultraspectral interferometer sounders such as the National Airborne Sounder Testbed-Interferometer (NAST-I) flew under the Suomi NPP satellite that carries the Crosstrack Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS). Herein we inter-compare the EDRs produced from different retrieval algorithms employed on these satellite and aircraft campaign data. The available radiosonde measurements together with the European Centre for Medium-Range Weather Forecasts (ECMWF) analyses are used to assess atmospheric temperature and moisture retrievals from the aircraft and satellite platforms. Preliminary results of this experiment under a winter, Arctic environment are presented

Use of Vegetation Health Data for Estimation of Aus Rice Yield in Bangladesh

Rice is a vital staple crop for Bangladesh and surrounding countries, with interannual variation in yields depending on climatic conditions. We compared Bangladesh yield of aus rice, one of the main varieties grown, from official agricultural statistics with Vegetation Health (VH) Indices [Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI)] computed from Advanced Very High Resolution Radiometer (AVHRR) data covering a period of 15 years (1991–2005). A strong correlation was found between aus rice yield and VCI and VHI during the critical period of aus rice development that occurs during March–April (weeks 8–13 of the year), several months in advance of the rice harvest. Stepwise principal component regression (PCR) was used to construct a model to predict yield as a function of critical-period VHI. The model reduced the yield prediction error variance by 62% compared with a prediction of average yield for each year. Remote sensing is a valuable tool for estimating rice yields well in advance of harvest and at a low cost

Calibration of Temperature in the Lower Stratosphere from Microwave Measurements Using COSMIC Radio Occultation Data: Preliminary Results

Accurate, consistent, and stable observations from different satellite missions are crucial for climate change detection. In this study, we use Global Positioning System (GPS) Radio Occultation (RO) data from the early phase of the FORMOSAT-3/Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission, which was successfully launched on 15 April 2006, to inter-calibrate Temperature in the Lower Stratosphere (TLS) taken from Advanced Microwave Sounding Unit (AMSU) microwave measurements from different satellites for potential improvements of stratospheric temperature trend analysis. Because of the limited number of COSMIC soundings in the early phase of the mission, these results are considered preliminary. In this study, we use COSMIC RO data to simulate microwave brightness temperatures for comparison with AMSU Ch9 measurements (e.g., TLS) on board NOAA15, 16, and 18. Excellent correlation was found between synthetic COSMIC brightness temperatures (Tbs) and Tbs from NOAA15, NOAA16, and NOAA18, respectively. However, systematic differences on the order of 0.7 to 2 K were found between COSMIC and AMSU observations over Antarctica. Our results demonstrate that synthetic COSMIC Tbs are very useful in identifying inter-satellite offsets among AMSU measurements from different satellites. To demonstrate the long-term stability of GPS RO data, we compare COSMIC dry temperature profiles to those from collocated CHAMP profiles, where CHAMP was launched in 2001. The fact that the CHAMPand COSMIC dry temperature difference between 500 and 10 hPa ranges from -0.35 K (at 10 hPa) to 0.25 K (at 30 hPa) and their mean difference is about -0.034 K demonstrates the long-term stability of GPS RO signals. In order to demonstrate the potential usage of the GPS RO calibrated AMSU Tbs to inter-calibrate other overlapping AMSU Tbs, we examine the uncertainty of the calibration coefficients derived from AMSU-GPS RO pairs. We found the difference between COSMIC calibrated AMSU Tbs and those from CHAMP to be in the range of __0.07 K with a 0.1 K standard deviation. This demonstrates the robustness of the calibration coefficients found from AMSU-GPS RO pairs and shows the potential to use the calibrated AMSU Tbs to calibrate other overlapping AMSU Tbs where no coincident GPS RO data are available