Development of a Green Roof Environmental Monitoring and Meteorological Network in New York City

Green roofs (with plant cover) are gaining attention in the United States as a versatile new environmental mitigation technology. Interest in data on the environmental performance of these systems is growing, particularly with respect to urban heat island mitigation and stormwater runoff control. We are deploying research stations on a diverse array of green roofs within the New York City area, affording a new opportunity to monitor urban environmental conditions at small scales. We show some green roof systems being monitored, describe the sensor selection employed to study energy balance, and show samples of selected data. These roofs should be superior to other urban rooftops as sites for meteorological stations

Generating Multi-Sensor Precipitation Estimates Over Radar Gap Areas

Generating a multi-sensor precipitation product over radar gap area is the objective of the present study. A merging approach is developed to improve Satellite-based Precipitation Estimates (SPE) by merging with ground-based Radar Rainfall (RR) estimates because remote satellites are the only source that can collect information from areas where are inaccessible by ground-based radar and/or rain gauge networks. The merging algorithm is capable of extending radar information from pixels with available RR to their neighboring pixels with no radar information by merging RR with SPE, which is, usually, available for all pixels. SPE is combined with RR using the weighting-based approach of Successive Correction Method (SCM) after local bias correction of SPE with respect to RR. High resolution satellite infrared-based rainfall estimates from the NESDIS Hydro Estimator algorithm (HE), at hourly 4 km × 4 km basis, is selected to be merged with radarbased NEXRAD Stage IV rainfall measurements to generate rainfall product for the radar gap areas. To be able to validate the generated rainfall against NEXRAD, different size areas with available radar rainfall are selected as radar gap regions. The developed merging technique is evaluated for several study cases in summer 2003 and 2004. The results show that generated rainfall for the radar gap areas are more correlated with RR (average 0.67) than original HE with RR (average 0.36) and the RMSE between merged and radar rainfall (average 2.8 mm) is less than the RMSE between satellite and radar rainfall (average 4.48 mm). And also, the pattern and intensity of the generated rainfall for radar gap area became more similar to the pattern and value of RR. In addition, the enhancement of the generated rainfall with respect to RR is more significant for high rainfall amounts

Potential of satellite-based land emissivity estimates for the detection of high-latitude freeze and thaw states

Reliable detection of freeze and thaw (FT) states is crucial for the terrestrial water cycle, biogeochemical transitions, carbon and methane feedback to the atmosphere, and for the surface energy budget and its associated impacts on the global climate system. This paper is novel in that for the first time a unique approach to examine the potential of passive microwave remotely sensed land emissivity and its added-values of being free from the atmospheric effects and being sensitive to surface characteristics is being applied to the detection of FT states for latitudes north of 35°N. Since accurate characterizations of the soil state are highly dependent on land cover types, a novel threshold-based approach specific to different land cover types is proposed for daily FT detection from the use of three years (August 2012 – July 2015) of the Advanced Microwave Scanning Radiometer – 2 land emissivity estimates. Ground-based soil temperature observations are used as reference to develop threshold values for FT states. Preliminary evaluation of the proposed approach with independent ground observations over Alaska for the year 2015 shows that the use of land emissivity estimates for high-latitude FT detection is promising

A Comparison of MODIS/VIIRS Cloud Masks over Ice-Bearing River: On Achieving Consistent Cloud Masking and Improved River Ice Mapping

The capability of frequently and accurately monitoring ice on rivers is important, since it may be possible to timely identify ice accumulations corresponding to ice jams. Ice jams are dam-like structures formed from arrested ice floes, and may cause rapid flooding. To inform on this potential hazard, the CREST River Ice Observing System (CRIOS) produces ice cover maps based on MODIS and VIIRS overpass data at several locations, including the Susquehanna River. CRIOS uses the respective platform’s automatically produced cloud masks to discriminate ice/snow covered grid cells from clouds. However, since cloud masks are produced using each instrument’s data, and owing to differences in detector performance, it is quite possible that identical algorithms applied to even nearly identical instruments may produce substantially different cloud masks. Besides detector performance, cloud identification can be biased due to local (e.g., land cover), viewing geometry, and transient conditions (snow and ice). Snow/cloud confusions and large view angles can result in substantial overestimates of clouds and ice. This impacts algorithms, such as CRIOS, since false cloud cover precludes the determination of whether an otherwise reasonably cloud free grid consists of water or ice. Especially for applications aiming to frequently classify or monitor a location it is important to evaluate cloud masking, including false cloud detections. We present an assessment of three cloud masks via the parameter of effective revisit time. A ~100 km stretch of up to 1.6 km wide river was examined with daily data sampled at 500 m resolution, examined over 317 days during winter. Results show that there are substantial differences between each of the cloud mask products, especially while the river bears ice. A contrast-based cloud screening approach was found to provide improved and consistent cloud and ice identification within the reach (95%–99% correlations, and 3%–7% mean absolute differences) between the independently observing platforms. River ice was also detected accurately (proportion correct 95%–100%) and more frequently. Owing to cross-platform compositing, it is possible to obtain an effective revisit time of 2.8 days and further error reductions

Validation of NOAA-Interactive Multisensor Snow and Ice Mapping System (IMS) by Comparison with Ground-Based Measurements over Continental United States

In this study, daily maps of snow cover distribution and sea ice extent produced by NOAA’s interactive multisensor snow and ice mapping system (IMS) were validated using in situ snow depth data from observing stations obtained from NOAA’s National Climatic Data Center (NCDC) for calendar years 2006 to 2010. IMS provides daily maps of snow and sea ice extent within the Northern Hemisphere using data from combination of geostationary and polar orbiting satellites in visible, infrared and microwave spectrums. Statistical correspondence between the IMS and in situ point measurements has been evaluated assuming that ground measurements are discrete and continuously distributed over a 4 km IMS snow cover maps. Advanced Very High Resolution Radiometer (AVHRR) land and snow classification data are supplemental datasets used in the further analysis of correspondence between the IMS product and in situ measurements. The comparison of IMS maps with in situ snow observations conducted over a period of four years has demonstrated a good correspondence of the data sets. The daily rate of agreement between the products mostly ranges between 80% and 90% during the Northern Hemisphere through the winter seasons when about a quarter to one third of the territory of continental US is covered with snow. Further, better agreement was observed for stations recording higher snow depth. The uncertainties in validation of IMS snow product with stationed NCDC data were discussed

Evaluation of VIIRS Land Surface Temperature Using CREST-SAFE Air, Snow Surface, and Soil Temperature Data

In this study, the Visible Infrared Imager Radiometer Suite (VIIRS) Land Surface Temperature (LST) Environmental Data Record (EDR) was evaluated against snow surface (T-skin) and near-surface air temperature (T-air) ground observations recorded at the Cooperative Remote Sensing Science and Technology Center—Snow Analysis and Field Experiment (CREST-SAFE), located in Caribou, ME, USA during the winters of 2013 and 2014. The satellite LST corroboration of snow-covered areas is imperative because high-latitude regions are often physically inaccessible and there is a need to complement the data from the existing meteorological station networks. T-skin is not a standard meteorological parameter commonly observed at synoptic stations. Common practice is to measure surface infrared emission from the land surface at research stations across the world that allow for estimating ground-observed LST. Accurate T-skin observations are critical for estimating latent and sensible heat fluxes over snow-covered areas because the incoming and outgoing radiation fluxes from the snow mass and T-air make the snow surface temperature different from the average snowpack temperature. Precise characterization of the LST using satellite observations is an important issue because several climate and hydrological models use T-skin as input. Results indicate that T-air correlates better than T-skin with VIIRS LST data and that the accuracy of nighttime LST retrievals is considerably better than that of daytime. Based on these results, empirical relationships to estimate T-air and T-skin for clear-sky conditions from remotely-sensed (RS) LST were derived. Additionally, an empirical formula to correct cloud-contaminated RS LST was developed

A Multi-temporal Analysis of AMSR-E Data for Flood and Discharge Monitoring during the 2008 Flood in Iowa

The objective of this work is to demonstrate the potential of using passive microwave data to monitor flood and discharge conditions and to infer watershed hydraulic and hydrologic parameters. The case study is the major flood in Iowa in summer 2008. A new Polarisation Ratio Variation Index (PRVI) was developed based on a multi-temporal analysis of 37 GHz satellite imagery from the Advanced Microwave Scanning Radiometer (AMSR-E) to calculate and detect anomalies in soil moisture and/or inundated areas. The Robust Satellite Technique (RST) which is a change detection approach based on the analysis of historical satellite records was adopted. A rating curve has been developed to assess the relationship between PRVI values and discharge observations downstream. A time-lag term has been introduced and adjusted to account for the changing delay between PRVI and streamflow. Moreover, the Kalman filter has been used to update the rating curve parameters in near real time. The temporal variability of the b exponent in the rating curve formula shows that it converges toward a constant value. A consistent 21-day time lag, very close to an estimate of the time of concentration, was obtained. The agreement between observed discharge downstream and estimated discharge with and without parameters adjustment was 65 and 95%, respectively. This demonstrates the interesting role that passive microwave can play in monitoring flooding and wetness conditions and estimating key hydrologic parameters

Fine Structure in Manhattan’s Daytime Urban Heat Island: A New Dataset

A street-level temperature and humidity dataset, with high resolution spatial and temporal components, has been created for the island of Manhattan, suitable for use by the urban health and modelling communities. It consists of a set of pedestrian measurements over the course of two summers converted into anomaly maps, and a set of ten light-post mounted installations measuring temperature, relative humidity, and illumination at three minute intervals over three months. The quality control and data reduction, used to produce the anomaly maps, is described, and the relationships between spatial and temporal variability are investigated. The data sets are available for download via the project Web site