scholarly journals National Weather Service Data Needs for Short-Term Forecasts and the Role of Unmanned Aircraft in Filling the Gap: Results from a Nationwide Survey

2021 ◽  
pp. 1-36
Author(s):  
Adam L. Houston ◽  
Lisa M. Pytlikzillig ◽  
Janell C. Walther
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Unmanned Aircraft ◽  
Short Term ◽  
National Weather Service ◽  
Surveillance Network ◽  
Critical Data ◽  
Data Gaps ◽  
Weather Service

AbstractInclusion of unmanned aircraft systems (UAS) into the weather surveillance network has the potential to improve short-term (< 1 day) weather forecasts through direct integration of UAS-collected data into the forecast process and assimilation into numerical weather prediction models. However, one of the primary means by which the value of any new sensing platform can be assessed is through consultation with principal stakeholders. National Weather Service (NWS) forecasters are principal stakeholders responsible for the issuance of short-term forecasts. The purpose of the work presented here is to use results from a survey of 630 NWS forecasters to assess critical data gaps that impact short-term forecast accuracy, and explore the potential role of UAS in filling these gaps.NWS forecasters view winter precipitation, icing, flood, lake-effect/enhanced snow, turbulence, and waves as the phenomena principally impacted by data gaps. Of the ten high-priority weather-related characteristics that need to be observed to fill critical data gaps, seven are either measures of precipitation or related to precipitation-producing phenomena. The three most important UAS capabilities/characteristics required for useful data for weather forecasting are real- or near-real-time data, the ability to integrate UAS data with additional data gathered by other systems, and UASs equipped with cameras to verify forecasts and monitor weather. Of the three operation modes offered for forecasters to consider, targeted surveillance is considered to be the most important compared to fixed site profiling or transects between fixed sites.

scholarly journals Initial Assessment of Unmanned Aircraft System Characteristics Required to Fill Data Gaps for Short-term Forecasts: Results from Focus Groups and Interviews

2020 ◽  
pp. 111-120
Author(s):  
Adam L. Houston ◽  
Janell C. Walther ◽  
Lisa M. Pytlikzillig ◽  
Jake Kawamoto
Keyword(s):  
Focus Groups ◽  
Deep Convection ◽  
Nationwide Survey ◽  
Unmanned Aircraft ◽  
Initial Assessment ◽  
State Variables ◽  
Short Term ◽  
Surveillance Network ◽  
Environmental Properties ◽  
Data Gaps

The integration of unmanned aircraft systems (UAS) into the weather surveillance network must be guided by the data needs of the principal stakeholders. This work aims to assess data needs/gaps for short-term forecasts (<1-day lead time) issued by the National Weather Service (NWS) and then identify UAS characteristics required to fill these gaps. Results from focus groups and interviews of forecasters in the central United States are presented. Participant verbal responses were coded and then categorized into a set of 25 unique features. Each feature was classified according to four characteristics: 1) environmental properties that need to be measured to represent a given feature, 2) flight type (vertical profile, horizontal transect, and/or survey) 3) flight height required to measure the environmental properties, and 4) relevance of feature to the forecasting of deep convection. Findings indicate the majority of identified features require measurement of typical state variables (temperature, moisture, and wind), but more than a third require visual imagery. Almost all of the features require either survey flight operations or vertical profiles. Additionally, 96% of the features require observations collected below 1000 m. Nearly two-thirds of the features are associated with deep convection. This work represents the first step towards establishing how UAS could be used to fill data gaps that exist for short-term forecasts issued by the NWS. The results stand alone in demonstrating the potential applications of UAS from the perspective of operational forecasters and have also informed ongoing efforts to develop a nationwide survey of forecasters.

scholarly journals The Mesoscale Predictability Experiment (MPEX)

2015 ◽  
Vol 96 (12) ◽  
pp. 2127-2149 ◽  
Author(s):  
Morris L. Weisman ◽  
Robert J. Trapp ◽  
Glen S. Romine ◽  
Chris Davis ◽  
Ryan Torn ◽  
...  
Keyword(s):  
Wide Spectrum ◽  
Weather Prediction ◽  
Early Morning ◽  
Basic Question ◽  
The Novel ◽  
Short Term ◽  
Convective Storms ◽  
Central United States ◽  
Convective Scale

Abstract The Mesoscale Predictability Experiment (MPEX) was conducted from 15 May to 15 June 2013 in the central United States. MPEX was motivated by the basic question of whether experimental, subsynoptic observations can extend convective-scale predictability and otherwise enhance skill in short-term regional numerical weather prediction. Observational tools for MPEX included the National Science Foundation (NSF)–National Center for Atmospheric Research (NCAR) Gulfstream V aircraft (GV), which featured the Airborne Vertical Atmospheric Profiling System mini-dropsonde system and a microwave temperature-profiling (MTP) system as well as several ground-based mobile upsonde systems. Basic operations involved two missions per day: an early morning mission with the GV, well upstream of anticipated convective storms, and an afternoon and early evening mission with the mobile sounding units to sample the initiation and upscale feedbacks of the convection. A total of 18 intensive observing periods (IOPs) were completed during the field phase, representing a wide spectrum of synoptic regimes and convective events, including several major severe weather and/or tornado outbreak days. The novel observational strategy employed during MPEX is documented herein, as is the unique role of the ensemble modeling efforts—which included an ensemble sensitivity analysis—to both guide the observational strategies and help address the potential impacts of such enhanced observations on short-term convective forecasting. Preliminary results of retrospective data assimilation experiments are discussed, as are data analyses showing upscale convective feedbacks.

scholarly journals The role of urban boundary layer investigated by high resolution models and ground based observations in Rome area: a step for understanding parameterizations potentialities

2013 ◽  
Vol 6 (3) ◽  
pp. 5297-5344
Author(s):  
E. Pichelli ◽  
R. Ferretti ◽  
M. Cacciani ◽  
A. M. Siani ◽  
V. Ciardini ◽  
...  
Keyword(s):  
Boundary Layer ◽  
High Resolution ◽  
Urban Areas ◽  
Prediction Models ◽  
Weather Prediction ◽  
Sonic Anemometer ◽  
Lower Atmosphere ◽  
Area Of Interest ◽  
The Impact

Abstract. The urban forcing on thermo-dynamical conditions can largely influences local evolution of the atmospheric boundary layer. Urban heat storage can produce noteworthy mesoscale perturbations of the lower atmosphere. The new generations of high-resolution numerical weather prediction models (NWP) is nowadays largely applied also to urban areas. It is therefore critical to reproduce correctly the urban forcing which turns in variations of wind, temperature and water vapor content of the planetary boundary layer (PBL). WRF-ARW, a new model generation, has been used to reproduce the circulation in the urban area of Rome. A sensitivity study is performed using different PBL and surface schemes. The significant role of the surface forcing in the PBL evolution has been verified by comparing model results with observations coming from many instruments (LiDAR, SODAR, sonic anemometer and surface stations). The crucial role of a correct urban representation has been demonstrated by testing the impact of different urban canopy models (UCM) on the forecast. Only one of three meteorological events studied will be presented, chosen as statistically relevant for the area of interest. The WRF-ARW model shows a tendency to overestimate vertical transmission of horizontal momentum from upper levels to low atmosphere, that is partially corrected by local PBL scheme coupled with an advanced UCM. Depending on background meteorological scenario, WRF-ARW shows an opposite behavior in correctly representing canopy layer and upper levels when local and non local PBL are compared. Moreover a tendency of the model in largely underestimating vertical motions has been verified.

scholarly journals Factors Influencing the Performance of Regression-Based Statistical Postprocessing Models for Short-Term Precipitation Forecasts

2019 ◽  
Vol 34 (6) ◽  
pp. 2067-2084
Author(s):  
Wentao Li ◽  
Qingyun Duan ◽  
Quan J. Wang
Keyword(s):  
Prediction Models ◽  
Probability Model ◽  
Weather Prediction ◽  
Joint Probability ◽  
Ensemble Spread ◽  
Probability Models ◽  
Short Term ◽  
Huai River ◽  
Censored Regression Model ◽  
Distributional Regression

Abstract Statistical postprocessing models can be used to correct bias and dispersion errors in raw precipitation forecasts from numerical weather prediction models. In this study, we conducted experiments to investigate four factors that influence the performance of regression-based postprocessing models with normalization transformations for short-term precipitation forecasts. The factors are 1) normalization transformations, 2) incorporation of ensemble spread as a predictor in the model, 3) objective function for parameter inference, and 4) two postprocessing schemes, including distributional regression and joint probability models. The experiments on the first three factors are based on variants of a censored regression model with conditional heteroscedasticity (CRCH). For the fourth factor, we compared CRCH as an example of the distributional regression with a joint probability model. The results show that the CRCH with normal quantile transformation (NQT) or power transformation performs better than the CRCH with log–sinh transformation for most of the subbasins in Huai River basin with a subhumid climate. The incorporation of ensemble spread as a predictor in CRCH models can improve forecast skill in our research region at short lead times. The influence of different objective functions (minimum continuous ranked probability score or maximum likelihood) on postprocessed results is limited to a few relatively dry subbasins in the research region. Both the distributional regression and the joint probability models have their advantages, and they are both able to achieve reliable and skillful forecasts.

scholarly journals Post-processing rainfall forecasts from numerical weather prediction models for short-term streamflow forecasting

2013 ◽  
Vol 17 (9) ◽  
pp. 3587-3603 ◽  
Author(s):  
D. E. Robertson ◽  
D. L. Shrestha ◽  
Q. J. Wang
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Probability Distributions ◽  
Weather Prediction ◽  
Lead Times ◽  
Streamflow Forecasting ◽  
Post Processing ◽  
Short Term ◽  
Ensemble Forecasts ◽  
Numerical Weather

Abstract. Sub-daily ensemble rainfall forecasts that are bias free and reliably quantify forecast uncertainty are critical for flood and short-term ensemble streamflow forecasting. Post-processing of rainfall predictions from numerical weather prediction models is typically required to provide rainfall forecasts with these properties. In this paper, a new approach to generate ensemble rainfall forecasts by post-processing raw numerical weather prediction (NWP) rainfall predictions is introduced. The approach uses a simplified version of the Bayesian joint probability modelling approach to produce forecast probability distributions for individual locations and forecast lead times. Ensemble forecasts with appropriate spatial and temporal correlations are then generated by linking samples from the forecast probability distributions using the Schaake shuffle. The new approach is evaluated by applying it to post-process predictions from the ACCESS-R numerical weather prediction model at rain gauge locations in the Ovens catchment in southern Australia. The joint distribution of NWP predicted and observed rainfall is shown to be well described by the assumed log-sinh transformed bivariate normal distribution. Ensemble forecasts produced using the approach are shown to be more skilful than the raw NWP predictions both for individual forecast lead times and for cumulative totals throughout all forecast lead times. Skill increases result from the correction of not only the mean bias, but also biases conditional on the magnitude of the NWP rainfall prediction. The post-processed forecast ensembles are demonstrated to successfully discriminate between events and non-events for both small and large rainfall occurrences, and reliably quantify the forecast uncertainty. Future work will assess the efficacy of the post-processing method for a wider range of climatic conditions and also investigate the benefits of using post-processed rainfall forecasts for flood and short-term streamflow forecasting.

Sign in / Sign up

Export Citation Format

Share Document