scholarly journals Global Forecast Impact of Low Data Latency Infrared and Microwave Sounders Observations from Polar Orbiting Satellites

2020 ◽  
Vol 12 (14) ◽  
pp. 2193
Author(s):  
Young-Chan Noh ◽  
Agnes H. N. Lim ◽  
Hung-Lung Huang ◽  
Mitchell D. Goldberg
Keyword(s):  
Positive Impact ◽  
Weather Prediction ◽  
Surface Wind ◽  
Atmospheric Temperature ◽  
Time Frame ◽  
Satellite Measurements ◽  
The World ◽  
Nwp Model ◽  
Environmental Prediction ◽  
Data Latency

The Direct Broadcast Network (DBNet) provides near-real-time delivery of low-earth-orbiting (LEO) meteorological satellites to operational numerical weather prediction (NWP) systems that need short data cut-off times to allow for the assimilation of the most recent satellite measurements. The NWP model requires timely delivery of observations including atmospheric temperature, humidity, and surface wind vectors. The World Meteorological Organization (WMO) Space Program (WSP) recommends the data latency of no more than 20 min for the satellite measurements. Currently, not all DBNet stations are delivering satellite data within the 20-min time frame. In this study, the forecast impact of the observations of LEO satellite sounders with data latency of 20 min or less was evaluated using the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). Reducing the data latency up to 5 min increases the number of LEO infrared (IR) and microwave (MW) sounder observations delivered to the NCEP GFS data assimilation system by more than 20%. Overall, this study demonstrates a positive impact on the global weather forecasts when the IR and MW sounder data are delivered by 20 min anywhere in the world. Additional forecast benefits are not obvious for shorter data latency. Results from this study support the WSP recommendation of 20–minute data latency.

scholarly journals High-resolution numerical modeling of mesoscale island wakes and sensitivity to static topographic relief data

2015 ◽  
Vol 8 (8) ◽  
pp. 2645-2653 ◽  
Author(s):  
C. G. Nunalee ◽  
Á. Horváth ◽  
S. Basu
Keyword(s):  
High Resolution ◽  
Vortex Shedding ◽  
Weather Prediction ◽  
Surface Wind ◽  
Wrf Model ◽  
Data Sets ◽  
Data Set ◽  
Near Surface ◽  
Nwp Model ◽  
Height Data

Abstract. Recent decades have witnessed a drastic increase in the fidelity of numerical weather prediction (NWP) modeling. Currently, both research-grade and operational NWP models regularly perform simulations with horizontal grid spacings as fine as 1 km. This migration towards higher resolution potentially improves NWP model solutions by increasing the resolvability of mesoscale processes and reducing dependency on empirical physics parameterizations. However, at the same time, the accuracy of high-resolution simulations, particularly in the atmospheric boundary layer (ABL), is also sensitive to orographic forcing which can have significant variability on the same spatial scale as, or smaller than, NWP model grids. Despite this sensitivity, many high-resolution atmospheric simulations do not consider uncertainty with respect to selection of static terrain height data set. In this paper, we use the Weather Research and Forecasting (WRF) model to simulate realistic cases of lower tropospheric flow over and downstream of mountainous islands using the default global 30 s United States Geographic Survey terrain height data set (GTOPO30), the Shuttle Radar Topography Mission (SRTM), and the Global Multi-resolution Terrain Elevation Data set (GMTED2010) terrain height data sets. While the differences between the SRTM-based and GMTED2010-based simulations are extremely small, the GTOPO30-based simulations differ significantly. Our results demonstrate cases where the differences between the source terrain data sets are significant enough to produce entirely different orographic wake mechanics, such as vortex shedding vs. no vortex shedding. These results are also compared to MODIS visible satellite imagery and ASCAT near-surface wind retrievals. Collectively, these results highlight the importance of utilizing accurate static orographic boundary conditions when running high-resolution mesoscale models.

Improvements in Nonconvective Aviation Turbulence Prediction for the World Area Forecast System

2018 ◽  
Vol 99 (11) ◽  
pp. 2295-2311 ◽  
Author(s):  
Jung-Hoon Kim ◽  
Robert Sharman ◽  
Matt Strahan ◽  
Joshua W. Scheck ◽  
Claire Bartholomew ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Measurement Data ◽  
Energy Dissipation Rate ◽  
Civil Aviation ◽  
Observation Data ◽  
Forecast System ◽  
Spatial Gradients ◽  
The World ◽  
Nwp Model ◽  
The Individual

AbstractFor the next generation of the World Area Forecast System (WAFS), the global Graphical Turbulence Guidance (G-GTG) has been developed using global numerical weather prediction (NWP) model outputs as an input to compute a set of turbulence diagnostics, identifying strong spatial gradients of meteorological variables associated with clear-air turbulence (CAT) and mountain-wave turbulence (MWT). The G-GTG provides an atmospheric turbulence intensity metric of energy dissipation rate (EDR) to the 1/3 power (m2/3 s–1), which is the International Civil Aviation Organization (ICAO) standard for aircraft reporting. Deterministic CAT and MWT EDR forecasts are derived from ensembles of calibrated multiple CAT and MWT diagnostics, respectively, with the final forecast provided by the gridpoint-by-gridpoint maximum of the CAT and MWT ensemble means. In addition, a probabilistic EDR forecast is produced by the percentage agreement of the individual CAT and MWT diagnostics that exceed a certain EDR threshold for turbulence (i.e., multidiagnostic ensemble). Objective evaluations of the G-GTG against global in situ EDR measurement data show that both deterministic and probabilistic G-GTG significantly improve the current WAFS CAT product, mainly because the G-GTG takes into account turbulence from various sources related to CAT and MWT. The probabilistic G-GTG forecast is more reliable at predicting light-or-greater (EDR > 0.15)- than moderate-or-greater (EDR > 0.22)-level turbulence, although it suffers from overforecasting. This will be improved in the future when we use this methodology with NWP ensembles and more observation data will be available for calibration. We expect that the new G-GTG forecasts will be beneficial to aviation users globally.

The POWER Experiment: Impact of Assimilation of a Network of Coastal Wind Profiling Radars on Simulating Offshore Winds in and above the Wind Turbine Layer

2016 ◽  
Vol 31 (4) ◽  
pp. 1071-1091 ◽  
Author(s):  
Irina V. Djalalova ◽  
Joseph Olson ◽  
Jacob R. Carley ◽  
Laura Bianco ◽  
James M. Wilczak ◽  
...  
Keyword(s):  
New England ◽  
Gulf Of Maine ◽  
Negative Impact ◽  
Positive Impact ◽  
Weather Prediction ◽  
Department Of Energy ◽  
Offshore Wind ◽  
Model Skill ◽  
Nwp Model ◽  
The Impact

Abstract During the summer of 2004 a network of 11 wind profiling radars (WPRs) was deployed in New England as part of the New England Air Quality Study (NEAQS). Observations from this dataset are used to determine their impact on numerical weather prediction (NWP) model skill at simulating coastal and offshore winds through data-denial experiments. This study is a part of the Position of Offshore Wind Energy Resources (POWER) experiment, a Department of Energy (DOE) sponsored project that uses National Oceanic and Atmospheric Administration (NOAA) models for two 1-week periods to measure the impact of the assimilation of observations from 11 inland WPRs. Model simulations with and without assimilation of the WPR data are compared at the locations of the inland WPRs, as well as against observations from an additional WPR and a high-resolution Doppler lidar (HRDL) located on board the Research Vessel Ronald H. Brown (RHB), which cruised the Gulf of Maine during the NEAQS experiment. Model evaluation in the lowest 2 km above the ground shows a positive impact of the WPR data assimilation from the initialization time through the next five to six forecast hours at the WPR locations for 12 of 15 days analyzed, when offshore winds prevailed. A smaller positive impact at the RHB ship track was also confirmed. For the remaining three days, during which time there was a cyclone event with strong onshore wind flow, the assimilation of additional observations had a negative impact on model skill. Explanations for the negative impact are offered.

scholarly journals Intercalibration of Backscatter Measurements among Ku-Band Scatterometers Onboard the Chinese HY-2 Satellite Constellation

2021 ◽  
Vol 13 (23) ◽  
pp. 4783
Author(s):  
Zhixiong Wang ◽  
Juhong Zou ◽  
Youguang Zhang ◽  
Ad Stoffelen ◽  
Wenming Lin ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Surface Wind ◽  
Double Difference ◽  
Satellite Orbits ◽  
Wind Speeds ◽  
Sea Surface Wind ◽  
Geophysical Model ◽  
Geophysical Model Function ◽  
Nwp Model ◽  
Ku Band

The Chinese HY-2D satellite was launched on 19 May 2021, carrying a Ku-band scatterometer. Together with the operating scatterometers onboard the HY-2B and HY-2C satellites, the HY-2 series scatterometer constellation was built, constituting different satellite orbits and hence opportunity for mutual intercomparison and intercalibration. To achieve intercalibration of backscatter measurements for these scatterometers, this study presents and performs three methods including: (1) direct comparison using collocated measurements, in which the nonlinear calibrations can also be derived; (2) intercalibration over the Amazon rainforest; (3) and the double-difference technique based on backscatter simulations over the global oceans, in which a geophysical model function and numerical weather prediction (NWP) model winds are needed. The results obtained using the three methods are comparable, i.e., the differences among them are within 0.1 dB. The intercalibration results are validated by comparing the HY-2 series scatterometer wind speeds with NWP model wind speeds. The curves of wind speed bias for the HY-2 series scatterometers are quite similar, particularly in wind speeds ranging from 4 to 20 m/s. Based on the well-intercalibrated backscatter measurements, consistent sea surface wind products from HY-2 series scatterometers can be produced, and greatly benefit data applications.

scholarly journals Current gust forecasting techniques, developments and challenges

2018 ◽  
Vol 15 ◽  
pp. 159-172 ◽  
Author(s):  
Peter Sheridan
Keyword(s):  
Structural Damage ◽  
Weather Prediction ◽  
Regional Scale ◽  
Surface Wind ◽  
Learning Approaches ◽  
Probabilistic Prediction ◽  
Near Surface ◽  
Advantages And Disadvantages ◽  
Boundary Layer Turbulence ◽  
Nwp Model

Abstract. Gusts represent the component of wind most likely to be associated with serious hazards and structural damage, representing short-lived extremes within the spectrum of wind variation. Of interest both for short range forecasting and for climatological and risk studies, this is also reflected in the variety of methods used to predict gusts based on various static and dynamical factors of the landscape and atmosphere. The evolution of Numerical Weather Prediction (NWP) models has delivered huge benefits from increasingly accurate forecasts of mean near-surface wind, with which gusts broadly scale. Techniques for forecasting gusts rely on parametrizations based on a physical understanding of boundary layer turbulence, applied to NWP model fields, or statistical models and machine learning approaches trained using observations, each of which brings advantages and disadvantages. Major shifts in the nature of the information available from NWP models are underway with the advent of ever-finer resolution and ensembles increasingly employed at the regional scale. Increases in the resolution of operational NWP models mean that phenomena traditionally posing a challenge for gust forecasting, such as convective cells, sting jets and mountain lee waves may now be at least partially represented in the model fields. This advance brings with it significant new questions and challenges, such as concerning: the ability of traditional gust prediction formulations to continue to perform as phenomena associated with gusty conditions become increasingly resolved; the extent to which differences in the behaviour of turbulence associated with each phenomenon need to be accommodated in future gust prediction methods. A similar challenge emerges from the increasing, but still partial resolution of terrain detail in NWP models; the speed-up of the mean wind over resolved hill tops may be realistic, but may have negative impacts on the performance of gust forecasting using current methods. The transition to probabilistic prediction using ensembles at the regional level means that considerations such as these must also be carried through to the aggregation and post-processing of ensemble members to produce the final forecast. These issues and their implications are discussed.

scholarly journals Opportunistic sensing with recreational hot-air balloon flights

2020 ◽  
pp. 1
Author(s):  
Evert I.F. de Bruijn ◽  
Fred C. Bosveld ◽  
Siebren de Haan ◽  
Albert A.M. Holtslag
Keyword(s):  
Positive Impact ◽  
Weather Prediction ◽  
Third Party ◽  
Wind Profiler ◽  
Meteorological Tower ◽  
Mountain Valley ◽  
Hot Air ◽  
Wind Measurements ◽  
New Type ◽  
Nwp Model

AbstractWe report about a new third party observation, namely wind measurements derived from Hot-Air Balloon (HAB) tracks. At first we compare the HAB winds with wind measurements from a meteorological tower and a radio acoustic wind profiler, both situated at the topographically flat Cabauw observatory in the Netherlands. To explore the potential of this new type of wind observation in other topographies, we present an intriguing HAB flight in Austria with a spectacular mountain-valley circulation. Subsequently, we compare the HAB data with a Numerical Weather Prediction (NWP) model during 2011-2013 and the standard deviation of the wind speed is 2.3 ms−1. Finally we show results from a data-assimilation feasibility experiment that reveals that HAB wind information can have a positive impact on a hindcasted NWP trajectory.

scholarly journals GEOVISUAL ANALYTICS ON THE VERIFICATION OF THE PAGASA OPERATIONAL NUMERICAL WEATHER PREDICTION MODEL RAINFALL FORECAST

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 215-222
Author(s):  
R. A. A. Flores
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Model Performance ◽  
Time Frame ◽  
Model Verification ◽  
Climate Type ◽  
Surface Precipitation ◽  
Numerical Weather ◽  
Geovisual Analytics ◽  
Nwp Model

Abstract. Assessment of NWP model performance is an integral part of operational forecasting as well as in research and development. Understanding the bias propagation of an NWP model and how it propagates across space can provide more insight in determining underlying causes and weaknesses not easily determined in traditional methods. The study aims to introduce the integration of the spatial distribution of error in interpreting model verification results by assessing how well the operational numerical weather prediction system of PAGASA captures the country’s weather pattern in each of its climate type. It also discusses improvements in model performance throughout the time-frame of analysis. Error propagation patterns were identified using Geovisual Analytics to allow comparison of verification scores among individual stations. The study concluded that a major update in the physics parameterization of the model in 2016 and continued minor updates in the following years, surface precipitation forecasts greatly improved from an average RMSE of 9.3, MAE of 3.2 and Bias of 1.36 in 2015 to an RMSE of 7.9, MAE of 2.5 and bias of −0.63 in 2018.

From InSAR derived relative tropospheric Slant Total Delay maps to absolute Zenith Total Delay maps: comparisons between tropospheric delay products to define a strategy for meteorological applications.

2020 ◽  
Author(s):  
Stefano Barindelli ◽  
Andrea Gatti ◽  
Martina Lagasio ◽  
Marco Manzoni ◽  
Alessandra Mascitelli ◽  
...  
Keyword(s):  
Positive Impact ◽  
Weather Prediction ◽  
Circulation Model ◽  
Tropospheric Delay ◽  
Total Delay ◽  
Zenith Total Delay ◽  
Iterative Interpolation ◽  
Physically Based ◽  
The Difference ◽  
Nwp Model

<p>InSAR derived Atmospheric Phase Screens (APSs) contain the difference between the atmospheric delay along the SAR sensor line-of-sight of two acquisition epochs: the slave and the master epochs. Using estimates of the atmospheric state at the master epoch, coming from independent sources, the APSs can be transformed into maps of tropospheric Zenith Total Delay (ZTD), that is related to the columnar atmospheric water vapor content. Assimilation experiments of such products into numerical weather prediction (NWP) models have shown a positive impact in the prediction of convective storms.</p><p>In this work, a systematical comparison between various APS and ZTD products aims at determining the optimal procedure to go from APSs to InSAR-derived absolute ZTD maps, i.e. to estimate the master delay map. Two different approaches are compared.</p><p>The first is based on a stack of ZTD maps produced with the assimilation of GNSS ZTD observations into an NWP model. This acts as a physically based interpolator of the GNSS values, which have a spatial resolution much coarser than the InSAR APS one.</p><p>The second is based on a stack of ZTD maps derived by an Iterative Tropospheric Decomposition (ITD) model, as implemented in the GACOS service. In this case, the high-resolution ZTD maps are obtained by an iterative interpolation of a global atmospheric circulation model values and GNSS values where available.</p><p>The results of the comparisons and sensitivity tests on the number of ZTD maps needed to derive the unknown master delay map are shown.</p><p> </p><p> </p><p> </p><p><strong> </strong></p><p><strong> </strong></p>

Real-Time Water Vapor Maps from a GPS Surface Network: Construction, Validation, and Applications

2009 ◽  
Vol 48 (7) ◽  
pp. 1302-1316 ◽  
Author(s):  
Siebren de Haan ◽  
Iwan Holleman ◽  
Albert A. M. Holtslag
Keyword(s):  
Water Vapor ◽  
Standard Deviation ◽  
Real Time ◽  
Weather Prediction ◽  
Surface Wind ◽  
Step Method ◽  
Background Error ◽  
Surface Network ◽  
Nwp Model ◽  
Additional Value

Abstract In this paper the construction of real-time integrated water vapor (IWV) maps from a surface network of global positioning system (GPS) receivers is presented. The IWV maps are constructed using a two-dimensional variational technique with a persistence background that is 15 min old. The background error covariances are determined using a novel two-step method, which is based on the Hollingsworth–Lonnberg method. The quality of these maps is assessed by comparison with radiosonde observations and IWV maps from a numerical weather prediction (NWP) model. The analyzed GPS IWV maps have no bias against radiosonde observations and a small bias against NWP analysis and forecasts up to 9 h. The standard deviation with radiosonde observations is around 2 kg m−2, and the standard deviation with NWP increases with increasing forecast length (from 2 kg m−2 for the NWP analysis to 4 kg m−2 for a forecast length of 48 h). To illustrate the additional value of these real-time products for nowcasting, three thunderstorm cases are discussed. The constructed GPS IWV maps are combined with data from the weather radar, a lightning detection network, and surface wind observations. All cases show that the location of developing thunderstorms can be identified 2 h prior to initiation in the convergence of moist air.

scholarly journals Applying a Divisive Clustering Algorithm to a Large Ensemble for Medium-Range Forecasting at the Weather Prediction Center

2015 ◽  
Vol 30 (4) ◽  
pp. 873-891 ◽  
Author(s):  
Keith F. Brill ◽  
Anthony R. Fracasso ◽  
Christopher M. Bailey
Keyword(s):  
Clustering Algorithm ◽  
Weather Prediction ◽  
Time Frame ◽  
Discrete Fourier Transformation ◽  
Large Ensemble ◽  
Ensemble Mean ◽  
Medium Range ◽  
Clustering Approach ◽  
Environmental Prediction ◽  
Divisive Clustering

Abstract This article explores the potential advantages of using a clustering approach to distill information contained within a large ensemble of forecasts in the medium-range time frame. A divisive clustering algorithm based on the one-dimensional discrete Fourier transformation is described and applied to the 70-member combination of the 20-member National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) and the 50-member European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble. Cumulative statistical verification indicates that clusters selected objectively based on having the largest number of members do not perform better than the ECMWF ensemble mean. However, including a cluster in a blended forecast to maintain continuity or to nudge toward a preferred solution may be a reasonable strategy in some cases. In such cases, a cluster may be used to sharpen a forecast weakly depicted by the ensemble mean but favored in consideration of continuity, consistency, collaborative thinking, and/or the trend in the guidance. Clusters are often useful for depicting forecast solutions not apparent via the ensemble mean but supported by a subset of ensemble members. A specific case is presented to demonstrate the possible utility of a clustering approach in the forecasting process.

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