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NASA NTRS2015

Evaluation of the Snow Simulations from the Community Land Model, Version 4 (CLM4)

This paper evaluates the simulation of snow by the Community Land Model, version 4 (CLM4), the land model component of the Community Earth System Model, version 1.0.4 (CESM1.0.4). CLM4 was run in an offline mode forced with the corrected land-only replay of the Modern-Era Retrospective Analysis for Research and Applications (MERRA-Land) and the output was evaluated for the period from January 2001 to January 2011 over the Northern Hemisphere poleward of 30 deg N. Simulated snow-cover fraction (SCF), snow depth, and snow water equivalent (SWE) were compared against a set of observations including the Moderate Resolution Imaging Spectroradiometer (MODIS) SCF, the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover, the Canadian Meteorological Centre (CMC) daily snow analysis products, snow depth from the National Weather Service Cooperative Observer (COOP) program, and Snowpack Telemetry (SNOTEL) SWE observations. CLM4 SCF was converted into snow-cover extent (SCE) to compare with MODIS SCE. It showed good agreement, with a correlation coefficient of 0.91 and an average bias of -1.54 x 10(exp 2) sq km. Overall, CLM4 agreed well with IMS snow cover, with the percentage of correctly modeled snow-no snow being 94%. CLM4 snow depth and SWE agreed reasonably well with the CMC product, with the average bias (RMSE) of snow depth and SWE being 0.044m (0.19 m) and -0.010m (0.04 m), respectively. CLM4 underestimated SNOTEL SWE and COOP snow depth. This study demonstrates the need to improve the CLM4 snow estimates and constitutes a benchmark against which improvement of the model through data assimilation can be measured.

northern hemisphere↗

NASA NTRS2002

Land Surface Data Assimilation and the Gulf Coast Sea-Breeze

A technique has been developed for assimilating GOES-derived skin temperature tendencies and insolation into the surface energy budget equation of a mesoscale model so that the simulated rate of temperature change closely agrees with the satellite observations. A critical assumption of the technique is that the availability of moisture (either from the soil or vegetation) is the least known term in the model's surface energy budget. Therefore, the simulated latent heat flux, which is a function of surface moisture availability, is adjusted based upon differences between the modeled and satellite observed skin temperature tendencies. An advantage of this technique is that satellite temperature tendencies are assimilated in an energetically consistent manner that avoids energy imbalances and surface stability problems that arise from direct assimilation of surface shelter temperatures. The fact that the rate of change of the satellite skin temperature is used rather than the absolute temperature means that sensor calibration is not as critical. The focus of this paper is to examine how the satellite assimilation technique impacts simulations of near-surface meteorology on the 4- to 48-hour time scale. The technique is implemented within the PSU/NCAR MM5 V34 and applied on a 36-km CONUS domain and a 12-km nest centered over Oklahoma. Two Friday periods during 2000 were selected for study, one during the warm season (May-June) and the other in the cold season (Nov-Dec). Bulk verification statistics (BIAS and RMSE) of surface air temperature and dewpoint show that the assimilation technique can improve numerical simulations on both regional and continental scales. Comparison of the simulated surface energy and water fluxes with observations from Energy Balance Bowen Ratio measurements taken at CART/ARM sites in the Southern Great Plains showed the assimilation technique produces a realistic argument of the Bowen ratio in both seasons. Additional runs with the Oregon State University land surface scheme as implemented within MM5 will be made and comparisons with assimilation results will be presented at the meeting.

Lapenta, William M.↗

NASA NTRS1975

A comparative interregional analysis of selected data from LANDSAT-1 and EREP for the inventory and monitoring of natural ecosystems

Comparative statistics were presented on the capability of LANDSAT-1 and three of the Skylab remote sensing systems (S-190A, S-190B, S-192) for the recognition and inventory of analogous natural vegetations and landscape features important in resource allocation and management. Two analogous regions presenting vegetational zonation from salt desert to alpine conditions above the timberline were observed, emphasizing the visual interpretation mode in the investigation. An hierarchical legend system was used as the basic classification of all land surface features. Comparative tests were run on image identifiability with the different sensor systems, and mapping and interpretation tests were made both in monocular and stereo interpretation with all systems except the S-192. Significant advantage was found in the use of stereo from space when image analysis is by visual or visual-machine-aided interactive systems. Some cost factors in mapping from space are identified. The various image types are compared and an operational system is postulated.

Poulton, C. E.↗

NASA NTRS1977

Program Documentation for Elapsed Time to Oxygen and Hydrogen Caution and Warning System for Captive/active 1 and 3 Flights

High pressure gas storage simulation runs were made to compute the elapsed time from T-O disconnect (transfer to onboard reactant) to the caution and warning pressures for the primary oxygen (O2) and hydrogen (H2) systems of Orbiter Vehicle (OV) 101. The simulation runs were made for approach and landing test flights Captive/Active 1 and Captive/Active 3.

Hurst, J. E.↗

NASA NTRS1951

Analysis of the effects of boundary-layer control in the take-off and power-off landing performance characteristics of a liaison type of airplane

A performance analysis has been made to determine whether boundary-layer control by suction might reduce the minimum take-off and landing distances of a four-place or five-place airplane or a liaison type of airplane below those obtainable with conventional high-lift devices. The airplane was assumed to have a cruise duration of 5 hours at 60-percent power and to be operating from airstrips having a ground friction coefficient of 0.2 or a combined ground and braking coefficient of 0.4. The payload was fixed at 1500 pounds, the wing span was varied from 25 to 100 feet, the aspect ratio was varied from 5 to 15, and the power was varied from 300 to 1300 horsepower. Maximum lift coefficients of 5.0 and 2.8 were assumed for the airplanes with and without boundary-layer-control --equipment weight was included. The effects of the boundary-layer control on total take-off distance, total power-off landing distance, landing and take-off ground run, stalling speed, sinking speed, and gliding speed were determined.

Horton, Elmer A↗

NASA NTRS2001

Astronaut Photography of Coral Reefs

Astronaut photographs of tropical coastal areas may contain information on submerged features, including coral reefs, up to depths of about 15 m in clear waters. Previous research efforts have shown that astronaut photographs can aid in estimating coral reef locations and extent on national, regional and global scales, and allow characterization of major geomorphological rim and lagoon features (Andrefouet et al. 2000, in preparation). They can be combined with traditional satellite data to help distinguish between clouds and lagoon features such as pinnacles (Andrefouet and Robinson, in review). Furthermore, astronaut photographs may provide reef scientists and managers with information on the location and extent of river plumes and sediment run off, or facilitate identification of land cover types, including mangroves (Webb et al., in press). Photographs included in the section were selected based on several criteria. The primary consideration of the editors was that the photographs represent a worldwide distribution of coral reefs, have extremely low visual interference by cloud cover, and display a spatial scale reasonable for examining reef-related features. Once photographs were selected, they were digitized from 2nd generation copies. The color and contrast were hand corrected to an approximation of natural color (required to account for spectral differences between photographs due to the color sensitivities of films used, and differences in sun angle and exposure of the photographs). None of the photographs shown here have been georeferenced to correct them to a map projection and scale. Any distortions in features due to slightly oblique look angles when the photographs were taken through spacecraft windows remain. When feasible, near vertical photographs have been rotated so that north is toward the top. An approximate scale bar and north arrow have added using distinctive features on each photograph with reference to a 1:1,000,000 scale navigation chart. Astronaut photographs provide a unique source of moderate resolution reef remote sensing data because of their global coverage and (immediate) availability in the public domain. The database of photographs can be searched an browsed online and high-resolution digital copies of photographs in this atlas can be accessed via the Website of Earth Science and Image Analysis at NASA's Johnson Space Center:

Robinson, Julie A.↗

NASA NTRS2014

Snowfall Rate Retrieval using NPP ATMS Passive Microwave Measurements

Passive microwave measurements at certain high frequencies are sensitive to the scattering effect of snow particles and can be utilized to retrieve snowfall properties. Some of the microwave sensors with snowfall sensitive channels are Advanced Microwave Sounding Unit (AMSU), Microwave Humidity Sounder (MHS) and Advance Technology Microwave Sounder (ATMS). ATMS is the follow‐on sensor to AMSU and MHS. Currently, an AMSU and MHS based land snowfall rate (SFR) product is running operationally at NOAA/NESDIS. Based on the AMSU/MHS SFR, an ATMS SFR algorithm has been developed recently. The algorithm performs retrieval in three steps: snowfall detection, retrieval of cloud properties, and estimation of snow particle terminal velocity and snowfall rate. The snowfall detection component utilizes principal component analysis and a logistic regression model. The model employs a combination of temperature and water vapor sounding channels to detect the scattering signal from falling snow and derive the probability of snowfall (Kongoli et al., 2014). In addition, a set of NWP model based filters is also employed to improve the accuracy of snowfall detection. Cloud properties are retrieved using an inversion method with an iteration algorithm and a two‐stream radiative transfer model (Yan et al., 2008). A method developed by Heymsfield and Westbrook (2010) is adopted to calculate snow particle terminal velocity. Finally, snowfall rate is computed by numerically solving a complex integral. The ATMS SFR product is validated against radar and gauge snowfall data and shows that the ATMS algorithm outperforms the AMSU/MHS SFR.

Meng, Huan↗

NASA NTRS2015

NPP ATMS Snowfall Rate Product

Passive microwave measurements at certain high frequencies are sensitive to the scattering effect of snow particles and can be utilized to retrieve snowfall properties. Some of the microwave sensors with snowfall sensitive channels are Advanced Microwave Sounding Unit (AMSU), Microwave Humidity Sounder (MHS) and Advance Technology Microwave Sounder (ATMS). ATMS is the follow‐on sensor to AMSU and MHS. Currently, an AMSU and MHS based land snowfall rate (SFR) product is running operationally at NOAA/NESDIS. Based on the AMSU/MHS SFR, an ATMS SFR algorithm has been developed recently. The algorithm performs retrieval in three steps: snowfall detection, retrieval of cloud properties, and estimation of snow particle terminal velocity and snowfall rate. The snowfall detection component utilizes principal component analysis and a logistic regression model. The model employs a combination of temperature and water vapor sounding channels to detect the scattering signal from falling snow and derive the probability of snowfall (Kongoli et al., 2015). In addition, a set of NWP model based filters is also employed to improve the accuracy of snowfall detection. Cloud properties are retrieved using an inversion method with an iteration algorithm and a two‐stream radiative transfer model (Yan et al., 2008). A method developed by Heymsfield and Westbrook (2010) is adopted to calculate snow particle terminal velocity. Finally, snowfall rate is computed by numerically solving a complex integral. NCEP CMORPH analysis has shown that integration of ATMS SFR has improved the performance of CMORPH‐Snow. The ATMS SFR product is also being assessed at several NWS Weather Forecast Offices for its usefulness in weather forecast.

Forecasting↗

NASA NTRS2015

Snowfall Rate Retrieval Using Passive Microwave Measurements and Its Applications in Weather Forecast and Hydrology

(AMSU), Microwave Humidity Sounder (MHS) and Advance Technology Microwave Sounder (ATMS). ATMS is the follow‐on sensor to AMSU and MHS. Currently, an AMSU and MHS based land snowfall rate (SFR) product is running operationally at NOAA/NESDIS. Based on the AMSU/MHS SFR, an ATMS SFR algorithm has also been developed. The algorithm performs retrieval in three steps: snowfall detection, retrieval of cloud properties, and estimation of snow particle terminal velocity and snowfall rate. The snowfall detection component utilizes principal component analysis and a logistic regression model. It employs a combination of temperature and water vapor sounding channels to detect the scattering signal from falling snow and derives the probability of snowfall. Cloud properties are retrieved using an inversion method with an iteration algorithm and a two‐stream radiative transfer model. A method adopted to calculate snow particle terminal velocity. Finally, snowfall rate is computed by numerically solving a complex integral. The SFR products are being used mainly in two communities: hydrology and weather forecast. Global blended precipitation products traditionally do not include snowfall derived from satellites because such products were not available operationally in the past. The ATMS and AMSU/MHS SFR now provide the winter precipitation information for these blended precipitation products. Weather forecasters mainly rely on radar and station observations for snowfall forecast. The SFR products can fill in gaps where no conventional snowfall data are available to forecasters. The products can also be used to confirm radar and gauge snowfall data and increase forecasters' confidence in their prediction.

Meng, Huan↗

NASA NTRS2012

The Mars Science Laboratory (MSL) MMRTG In-Flight: A Power Update

The MSL Multi-Mission Radioisotope Thermoelectric Generator (MMRTG) was fueled on October 28, 2008 by the Department Of Energy (DOE) in preparation for a late 2009 launch. Shortly after, the MSL launch was delayed approximately 2 years until 2011. The fueled MMRTG was placed in storage to await the new date for liftoff. Occasional measurements of the MMRTG's power output were taken and compared with power predictions that pre-dated fueling. An error in the predictive models was quickly recognized and remedied. The predictions, while improved, carried significant uncertainty. This uncertainty did not deter the launch of MSL, but did alter the planned mission on the surface of Mars. Once launched, the MSL spacecraft provided a hi-fidelity telemetry stream measuring the generator's electrical and thermal performance. These data were used to update the predictive models and a new prediction of the performance of the MMRTG on the surface of Mars was run just before Entry, Descent, and Landing (EDL) at Mars. The MSL MMRTG is working extremely well, providing power above predictions and operating within its flight allowable temperature limits. The generator was producing approximately 114 W at the beginning of the surface mission. This paper will elaborate on power modeling for the MSL MMRTG along with a review of some of the data recorded from the MSL cruise to Mars, EDL, and the early days of the surface mission.

thermoelectric generator↗

NASA NTRS2013

The Mars Science Laboratory (MSL) MMRTG In-Flight: A Power Update

The MSL Multi-Mission Radioisotope Thermoelectric Generator (MMRTG) was fueled on October 28, 2008 by the Department Of Energy (DOE) in preparation for a late 2009 launch. Shortly after, the MSL launch was delayed approximately 2 years until 2011. The fueled MMRTG was placed in storage to await the new date for liftoff. Occasional measurements of the MMRTG's power output were taken and compared with power predictions that pre-dated fueling. An error in the predictive models was quickly recognized and remedied. The resultant predictions, while improved, carried significant uncertainty. This uncertainty did not deter the launch of MSL, but did alter the planned mission on the surface of Mars. Once launched, the MSL spacecraft provided a hi-fidelity telemetry stream measuring the generator's electrical and thermal performance. These data were used to update the predictive models and a new prediction of the performance of the MMRTG on the surface of Mars was run just before Entry, Descent, and Landing (EDL) at Mars. The MSL MMRTG is working extremely well, providing power above predictions and operating within its flight allowable temperature limits. The generator was producing approximately 114 W at the beginning of the surface mission. This paper will elaborate on power modeling for the MSL MMRTG along with a review of some of the data recorded from the MSL cruise to Mars, EDL, and the early days of the surface mission.

thermoelectric↗

NASA NTRSDate not supplied

Propagation of the MJO and Associated Moist Dynamics, and the Advantages of Enhanced Resolution in NASA’s GEOS-S2S Forecast System

This presentation explores how reliably the NASA’s new subseasonal to seasonal (S2S) forecast system represents the key features of the Madden-Julian Oscillation (MJO). This system is a fully coupled global model that includes the components of atmosphere, ocean, land, and sea ice. The model runs at an approximate spatial resolution of 0.5 degree for the atmosphere and 0.25 degree for the ocean. It employs a two-moment cloud microphysics to enable aerosol-cloud interaction and implements a novel atmosphere-ocean interface layer to enhance the representation of the diurnal warm and cool-skin layer.

Young-Kwon Lim↗

NASA NTRSDate not supplied

Propagation of the MJO and Associated Moist Dynamics, and the Advantages of Enhanced Resolution in NASA’s GEOS-S2S Forecast System

Young-Kwon Lim↗

NASA NTRS2010

An Assessment of the Potential Predictability of Drought Over the United States Based on Climate Model Simulations with Specified SST

The USCLIV AR working group on drought recently initiated a series of global climate model simulations forced with idealized SST anomaly patterns, designed to address a number of uncertainties regarding the impact of SST forcing and the role of land-atmosphere feedbacks on regional drought. The runs were done with several global atmospheric models including NASA/NSIPP-l, NCEP/GFS, GFDLlAM2, and NCAR CCM3 and CAM3.5. Specific questions that the runs are designed to address include: What are mechanisms that maintain drought across the seasonal cycle and from one year to the next. To what extent can droughts develop independently of ocean variability due to year-to-year memory that may be inherent to the land. What is the role of the different ocean basins? Here we focus on the potential predictability of drought conditions over the United States. Specific issues addressed include the seasonality and regionality of the signal-to-noise ratios associated with Pacific and Atlantic SST forcing, and the sensitivity of the results to the climatological stationary waves simulated by the different AGCMs.

Schubert, Siegfried↗

NASA NTRS2016

Transitioning Enhanced Land Surface Initialization and Model Verification Capabilities to the Kenya Meteorological Department (KMD)

Flooding, severe weather, and drought are key forecasting challenges for the Kenya Meteorological Department (KMD), based in Nairobi, Kenya. Atmospheric processes leading to convection, excessive precipitation and/or prolonged drought can be strongly influenced by land cover, vegetation, and soil moisture content, especially during anomalous conditions and dry/wet seasonal transitions. It is thus important to represent accurately land surface state variables (green vegetation fraction, soil moisture, and soil temperature) in Numerical Weather Prediction (NWP) models. The NASA SERVIR and the Short-term Prediction Research and Transition (SPoRT) programs in Huntsville, AL have established a working partnership with KMD to enhance its regional modeling capabilities. SPoRT and SERVIR are providing experimental land surface initialization datasets and model verification capabilities for capacity building at KMD. To support its forecasting operations, KMD is running experimental configurations of the Weather Research and Forecasting (WRF; Skamarock et al. 2008) model on a 12-km/4-km nested regional domain over eastern Africa, incorporating the land surface datasets provided by NASA SPoRT and SERVIR. SPoRT, SERVIR, and KMD participated in two training sessions in March 2014 and June 2015 to foster the collaboration and use of unique land surface datasets and model verification capabilities. Enhanced regional modeling capabilities have the potential to improve guidance in support of daily operations and high-impact weather and climate outlooks over Eastern Africa. For enhanced land-surface initialization, the NASA Land Information System (LIS) is run over Eastern Africa at ~3-km resolution, providing real-time land surface initialization data in place of interpolated global model soil moisture and temperature data available at coarser resolutions. Additionally, real-time green vegetation fraction (GVF) composites from the Suomi-NPP VIIRS instrument is being incorporated into the KMD-WRF runs, using the product generated by NOAA/NESDIS. Model verification capabilities are also being transitioned to KMD using NCAR's Model *Corresponding author address: Jonathan Case, ENSCO, Inc., 320 Sparkman Dr., Room 3008, Huntsville, AL, 35805. Email: Jonathan.Case-1@nasa.gov Evaluation Tools (MET; Brown et al. 2009) software in conjunction with a SPoRT-developed scripting package, in order to quantify and compare errors in simulated temperature, moisture and precipitation in the experimental WRF model simulations. This extended abstract and accompanying presentation summarizes the efforts and training done to date to support this unique regional modeling initiative at KMD. To honor the memory of Dr. Peter J. Lamb and his extensive efforts in bolstering weather and climate science and capacity-building in Africa, we offer this contribution to the special Peter J. Lamb symposium. The remainder of this extended abstract is organized as follows. The collaborating international organizations involved in the project are presented in Section 2. Background information on the unique land surface input datasets is presented in Section 3. The hands-on training sessions from March 2014 and June 2015 are described in Section 4. Sample experimental WRF output and verification from the June 2015 training are given in Section 5. A summary is given in Section 6, followed by Acknowledgements and References.

land surface modeling↗

NASA NTRS2014

Expansion of the Real-time Sport-land Information System for NOAA / National Weather Service Situational Awareness and Local Modeling Applications

The NASA Short-term Prediction Research and Transition (SPoRT) Center in Huntsville, AL (Jedlovec 2013; Ralph et al. 2013; Merceret et al. 2013) is running a real-time configuration of the Noah land surface model (LSM) within the NASA Land Information System (LIS) framework (hereafter referred to as the "SPoRT-LIS"). Output from the real-time SPoRT-LIS is used for (1) initializing land surface variables for local modeling applications, and (2) displaying in decision support systems for situational awareness and drought monitoring at select NOAA/National Weather Service (NWS) partner offices. The SPoRT-LIS is currently run over a domain covering the southeastern half of the Continental United States (CONUS), with an additional experimental real-time run over the entire CONUS and surrounding portions of southern Canada and northern Mexico. The experimental CONUS run incorporates hourly quantitative precipitation estimation (QPE) from the National Severe Storms Laboratory Multi- Radar Multi-Sensor (MRMS) product (Zhang et al. 2011, 2014), which will be transitioned into operations at the National Centers for Environmental Prediction (NCEP) in Fall 2014. This paper describes the current and experimental SPoRT-LIS configurations, and documents some of the limitations still remaining through the advent of MRMS precipitation analyses in the SPoRT-LIS land surface model (LSM) simulations. Section 2 gives background information on the NASA LIS and describes the realtime SPoRT-LIS configurations being compared. Section 3 presents recent work done to develop a training module on situational awareness applications of real-time SPoRT-LIS output. Comparisons between output from the two SPoRT-LIS runs are shown in Section 4, including a documentation of issues encountered in using the MRMS precipitation dataset. A summary and future work in given in Section 5, followed by acknowledgements and references.

Case, Jonathan L.↗

NASA NTRS2013

Comparison of Four Precipitation Forcing Datasets in Land Information System Simulations over the Continental U.S.

This paper and poster presented a description of the current real-time SPoRT-LIS run over the southeastern CONUS to provide high-resolution, land surface initialization grids for local numerical model forecasts at NWS forecast offices. The LIS hourly output also offers a supplemental dataset to aid in situational awareness for convective initiation forecasts, assessing flood potential, and monitoring drought at fine scales. It is a goal of SPoRT and several NWS forecast offices to expand the LIS to an entire CONUS domain, so that LIS output can be utilized by NWS Western Region offices, among others. To make this expansion cleanly so as to provide high-quality land surface output, SPoRT tested new precipitation datasets in LIS as an alternative forcing dataset to the current radar+gauge Stage IV product. Similar to the Stage IV product, the NMQ product showed comparable patterns of precipitation and soil moisture distribution, but suffered from radar gaps in the intermountain West, and incorrectly set values to zero instead of missing in the data-void regions of Mexico and Canada. The other dataset tested was the next-generation GOES-R QPE algorithm, which experienced a high bias in both coverage and intensity of accumulated precipitation relative to the control (NLDAS2), Stage IV, and NMQ simulations. The resulting root zone soil moisture was substantially higher in most areas.

Case, Jonathan L.↗

NASA NTRS2003

Use of MODIS Land and Sea Surface Temperatures to Initialize Mesoscale Models

As computer power continues to increase, mesoscale models are initialized at all hours of the day and continue to be run at higher and higher spatial resolutions. As a result, initializing land surface temperature can be problematic. The majority of research-based models are initialized at 00 and 12 UTC when upper air observations and reanalysis fields are available. The landsea surface temperatures are then set equal to the two-meter air temperature produced by the preprocessor analysis system. This particular procedure might be valid in the early morning hours just prior to sunrise, but it becomes less valid during the remainder of the diurnal cycle. Operational models, such as the Rapid Update Cycle run at the National Centers for Environmental Prediction (NCEP), are initialized every hour on a daily basis. This presents a unique challenge to the initial specification of the land temperature, especially during the first several hours of the solar heating cycle when land and overlying air temperatures are far from being the same. Another issue that needs to be addressed is the spatial variability of land surface temperature. By early next year, the NCEP operational Eta model will be employed at 8 km resolution. Methods to accurately specify the initial land surface temperature at such high resolution need to be explored. This paper presents the results of using data from the NASA Moderate Imaging Sensor aboard the TERRA Satellite to initialize land and sea surface temperatures within the Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) 5'th generation Mesoscale Model (MM5). We have simulated a northern Gulf Coast sea breeze case to demonstrate the utility of using the MODIS data to initialize both the land and sea surface temperature fields. Model grid configurations of 12-, 4-, and l-km are employed.

Arnold, James E.↗