scholarly journals Evaluations of Upper-Level Turbulence Diagnostics Performance Using the Graphical Turbulence Guidance (GTG) System and Pilot Reports (PIREPs) over East Asia

2011 ◽  
Vol 50 (9) ◽  
pp. 1936-1951 ◽  
Author(s):  
Jung-Hoon Kim ◽  
Hye-Yeong Chun ◽  
Robert D. Sharman ◽  
Teddie L. Keller
Keyword(s):  
East Asia ◽  
Lead Time ◽  
Large Scale ◽  
Weather Prediction ◽  
Analysis Data ◽  
Atmospheric Conditions ◽  
Jet Streams ◽  
Forecast Lead Time ◽  
Upper Level ◽  
Nwp Model

AbstractThe forecast skill of upper-level turbulence diagnostics is evaluated using available turbulence observations [viz., pilot reports (PIREPs)] over East Asia. The six years (2003–08) of PIREPs used in this study include null, light, and moderate-or-greater intensity categories. The turbulence diagnostics used are a subset of indices in the Graphical Turbulence Guidance (GTG) system. To investigate the optimal performance of the component GTG diagnostics and GTG combinations over East Asia, various statistical evaluations and sensitivity tests are performed. To examine the dependency of the GTG system on the operational numerical weather prediction (NWP) model, the GTG system is applied to both the Regional Data Assimilation and Prediction System (RDAPS) analysis data and Global Forecasting System (GFS) analysis and forecast data with 30-km and 0.3125° (T382) horizontal grid spacings. The dependency of the temporal variation in the PIREP and GFS data and the forecast lead time of the GFS-based GTG combination are also investigated. It is found that the forecasting performance of the GTG system varies with year and season according to the annual and seasonal variations in the large-scale atmospheric conditions over the East Asia region. The wintertime GTG skill is the highest, because most GTG component diagnostics are related to jet streams and upper-level fronts. The GTG skill improves as the number of PIREP samples and the vertical resolution of the underlying NWP analysis data increase, and the GTG performance decreases as the forecast lead time increases from 0 to 12 h.

scholarly journals A Review of Quantitative Precipitation Forecasts and Their Use in Short- to Medium-Range Streamflow Forecasting

2011 ◽  
Vol 12 (5) ◽  
pp. 713-728 ◽  
Author(s):  
Lan Cuo ◽  
Thomas C. Pagano ◽  
Q. J. Wang
Keyword(s):  
Lead Time ◽  
Weather Prediction ◽  
Research Field ◽  
Lead Times ◽  
Streamflow Forecasting ◽  
Streamflow Prediction ◽  
Forecast Lead Time ◽  
Quantitative Precipitation Forecasts ◽  
Active Research ◽  
Nwp Model

Abstract Unknown future precipitation is the dominant source of uncertainty for many streamflow forecasts. Numerical weather prediction (NWP) models can be used to generate quantitative precipitation forecasts (QPF) to reduce this uncertainty. The usability and usefulness of NWP model outputs depend on the application time and space scales as well as forecast lead time. For streamflow nowcasting (very short lead times; e.g., 12 h), many applications are based on measured in situ or radar-based real-time precipitation and/or the extrapolation of recent precipitation patterns. QPF based on NWP model output may be more useful in extending forecast lead time, particularly in the range of a few days to a week, although low NWP model skill remains a major obstacle. Ensemble outputs from NWP models are used to articulate QPF uncertainty, improve forecast skill, and extend forecast lead times. Hydrologic prediction driven by these ensembles has been an active research field, although operational adoption has lagged behind. Conversely, relatively little study has been done on the hydrologic component (i.e., model, parameter, and initial condition) of uncertainty in the streamflow prediction system. Four domains of research are identified: selection and evaluation of NWP model–based QPF products, improved QPF products, appropriate hydrologic modeling, and integrated applications.

scholarly journals Estimation of rainfall erosivity based on WRF-derived raindrop size distributions

2020 ◽  
Author(s):  
Qiang Dai ◽  
Jingxuan Zhu ◽  
Shuliang Zhang ◽  
Shaonan Zhu ◽  
Dawei Han ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Water Conservation ◽  
Large Scale ◽  
Weather Prediction ◽  
Wrf Model ◽  
Analysis Data ◽  
Potential Effect ◽  
Rainfall Erosivity ◽  
Size Distributions ◽  
Raindrop Size

Abstract. Soil erosion can cause various ecological problems, such as land degradation, soil fertility loss, and river siltation. Rainfall is the primary water-driving force for soil erosion and its potential effect on soil erosion is reflected by rainfall erosivity that relates to the raindrop kinetic energy (KE). As it is difficult to observe large-scale dynamic characteristics of raindrops, all the current rainfall erosivity models use the function based on rainfall amount to represent the raindrops KE. With the development of global atmospheric re-analysis data, numerical weather prediction (NWP) techniques become a promising way to estimate rainfall KE directly at regional and global scales with high spatial and temporal resolutions. This study proposed a novel method for large-scale and long-term rainfall erosivity investigations based on the Weather Research and Forecasting (WRF) model, avoiding errors caused by inappropriate rainfall–energy relationships and large-scale interpolation. We adopted three microphysical parameterizations schemes (Morrison, WDM6, and Thompson aerosol-aware [TAA]) to obtain raindrop size distributions, rainfall KE and rainfall erosivity, with validation by two disdrometers and 304 rain gauges around the United Kingdom. Among the three WRF schemes, TAA had the best performance compared with the disdrometers at a monthly scale. The results revealed that high rainfall erosivity occurred in the west coast area at the whole country scale during 2013–2017. The proposed methodology makes a significant contribution to improving large-scale soil erosion estimation and for better understanding microphysical rainfall–soil interactions to support the rational formulation of soil and water conservation planning.

scholarly journals Estimation of rainfall erosivity based on WRF-derived raindrop size distributions

2020 ◽  
Vol 24 (11) ◽  
pp. 5407-5422
Author(s):  
Qiang Dai ◽  
Jingxuan Zhu ◽  
Shuliang Zhang ◽  
Shaonan Zhu ◽  
Dawei Han ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Kinetic Energy ◽  
Water Conservation ◽  
Large Scale ◽  
Weather Prediction ◽  
Wrf Model ◽  
Analysis Data ◽  
Rainfall Erosivity ◽  
Size Distributions ◽  
Raindrop Size

Abstract. Soil erosion can cause various ecological problems, such as land degradation, soil fertility loss, and river siltation. Rainfall is the primary water-driven force for soil erosion, and its potential effect on soil erosion is reflected by rainfall erosivity that relates to the raindrop kinetic energy. As it is difficult to observe large-scale dynamic characteristics of raindrops, all the current rainfall erosivity models use the function based on rainfall amount to represent the raindrops' kinetic energy. With the development of global atmospheric re-analysis data, numerical weather prediction techniques become a promising way to estimate rainfall kinetic energy directly at regional and global scales with high spatial and temporal resolutions. This study proposed a novel method for large-scale and long-term rainfall erosivity investigations based on the Weather Research and Forecasting (WRF) model, avoiding errors caused by inappropriate rainfall–energy relationships and large-scale interpolation. We adopted three microphysical parameterizations schemes (Morrison, WDM6, and Thompson aerosol-aware) to obtain raindrop size distributions, rainfall kinetic energy, and rainfall erosivity, with validation by two disdrometers and 304 rain gauges around the United Kingdom. Among the three WRF schemes, Thompson aerosol-aware had the best performance compared with the disdrometers at a monthly scale. The results revealed that high rainfall erosivity occurred in the west coast area at the whole country scale during 2013–2017. The proposed methodology makes a significant contribution to improving large-scale soil erosion estimation and for better understanding microphysical rainfall–soil interactions to support the rational formulation of soil and water conservation planning.

scholarly journals Study of Clear Air Turbulence Related to Tropopause Folding over the Romanian Airspace

Atmosphere ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 1099
Author(s):  
Sabina Ștefan ◽  
Bogdan Antonescu ◽  
Ana Denisa Urlea ◽  
Livius Buzdugan ◽  
Meda Daniela Andrei ◽  
...  
Keyword(s):  
Large Scale ◽  
Lower Stratosphere ◽  
Meteorological Data ◽  
Horizontal Temperature Gradient ◽  
Jet Streams ◽  
Mountain Waves ◽  
Upper Troposphere ◽  
Air Turbulence ◽  
Upper Level ◽  
Tropopause Folding

Clear air turbulence (CAT) poses a significant threat to aviation. CAT usually occurs in the lower stratosphere and the upper troposphere. It is generally associated with large scale waves, mountain waves, jet streams, upper-level fronts and tropopause folds. Aircraft can experience CAT when flying in proximity of a tropopause fold. To better understand and diagnose tropopause fold- associated CAT we selected a series of cases from among those reported by pilots between June 2017 and December 2018 in the Romanian airspace. Data on turbulence were used in conjunction with meteorological data, satellite imagery, and vertical profiles. Additionally, a set of indices as Ellrod, horizontal temperature gradient, Dutton, and Brown were computed to diagnose CAT associated with tropopause folding. These indices were also analyzed to test the physics mechanisms that may explain the occurrence of severe turbulence. Results show that out of the 420 cases announced by pilots, severe turbulence was reported in 80 cases of which 13 were associated with tropopause folding.

scholarly journals Sensitivity of Cloud-Resolving Simulations of Warm-Season Convection to Cloud Microphysics Parameterizations

2007 ◽  
Vol 135 (8) ◽  
pp. 2854-2868 ◽  
Author(s):  
Changhai Liu ◽  
Mitchell W. Moncrieff
Keyword(s):  
Large Scale ◽  
Prediction Models ◽  
Weather Prediction ◽  
Warm Season ◽  
Cloud Microphysics ◽  
Mixed Phase ◽  
Fine Grid ◽  
Upper Level ◽  
Heating Profiles ◽  
Microphysical Parameterization

Abstract This paper investigates the effects of cloud microphysics parameterizations on simulations of warm-season precipitation at convection-permitting grid spacing. The objective is to assess the sensitivity of summertime convection predictions to the bulk microphysics parameterizations (BMPs) at fine-grid spacings applicable to the next generation of operational numerical weather prediction models. Four microphysical parameterization schemes are compared: simple ice (Dudhia), four-class mixed phase (Reisner et al.), Goddard five-class mixed phase (Tao and Simpson), and five-class mixed phase with graupel (Reisner et al.). The experimentation involves a 7-day episode (3–9 July 2003) of U.S. midsummer convection under moderate large-scale forcing. Overall, the precipitation coherency manifested as eastward-moving organized convection in the lee of the Rockies is insensitive to the choice of the microphysics schemes, and the latent heating profiles are also largely comparable among the BMPs. The upper-level condensate and cloudiness, upper-level radiative cooling/heating, and rainfall spectrum are the most sensitive, whereas the domain-mean rainfall rate and areal coverage display moderate sensitivity. Overall, the three mixed-phase schemes outperform the simple ice scheme, but a general conclusion about the degree of sophistication in the microphysics treatment and the performance is not achievable.

Uncertainty Evaluation in a Flood Forecasting Model Using JMA Numerical Weather Prediction

2009 ◽  
Vol 4 (4) ◽  
pp. 600-605 ◽  
Author(s):  
Hadi Kardhana ◽  
◽  
Akira Mano ◽  
Keyword(s):  
Numerical Weather Prediction ◽  
Lead Time ◽  
Weather Prediction ◽  
Japan Meteorological Agency ◽  
Rainfall Runoff ◽  
Flood Prediction ◽  
Numerical Weather ◽  
Forecast Lead Time ◽  
Rainfall Runoff Model ◽  
Runoff Model

Numerical weather prediction (NWP) is useful in flood prediction using a rainfall-runoff model. Uncertainty occurring in the forecast, however, adversely affects flood prediction accuracy, in addition to uncertainty inherent in the rainfall-runoff model. Clarifying this uncertainty and its magnitude is expected to lead to wider forecast applications. Taking the case of Japan’s Shichikashuku Dam, 6 flood events between 2002 and 2007 were analyzed. NWP was based on short-range forecasts by the Japan Meteorological Agency (JMA). The rainfall-runoff model is based on a distributed tank model. This research calculates uncertainty by identifying and quantifying the relative error of forecasts by a) NWP and b) the runoff model. Results showed that NAP is the main cause of flood forecast uncertainty. They also showed the correlation between forecast lead time and uncertainty. Uncertainty rises with longer lead time, corresponding to the magnitude of observed discharge and precipitation.

scholarly journals Low-level windshear associated with atmospheric boundary layer jets – Case studies

Atmósfera ◽  
2020 ◽  
Vol 34 (4) ◽  
pp. 461-490
Author(s):  
P. W. Chan ◽  
K. K. Hon ◽  
Q. S. Li
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Southern China ◽  
Weather Prediction ◽  
Northeast Monsoon ◽  
Jet Streams ◽  
Easterly Wind ◽  
Low Level ◽  
Reasonable Prediction ◽  
Nwp Model

Jet streams in the atmospheric boundary layer may lead to hazardous weather over southern China. In this paper, the jet-related low-level windshear to be encountered by an aircraft is documented. Two typical cases under the northeast monsoon regime are considered, namely, easterly jet disrupted by the mountains to the south of Hong Kong International Airport, and outbreak of monsoon surge that produces a low-level northeasterly jet. The Doppler Light Detection and Ranging (LIDAR) systems are found to capture the corresponding windshear features very well, e.g., consistent with pilot reports and flight data. They are useful in providing timely alert to the aircraft. In particular, the LIDAR captures a double jet structure in the atmospheric boundary layer for the easterly wind case, which has not been reported in the literature before. The physical mechanism for the occurrence of the double jet is yet to be revealed. Moreover, the performance of a high spatial resolution (200 m) numerical weather prediction (NWP) model in predicting the jet and the associated low-level windshear is studied. The model is found to provide reasonable prediction of the windshear features at a few hours ahead, and, for the cases studied, shows skills in providing timely alerts to the aircraft.

Evaluation of Forecasts of the Water Vapor Signature of Atmospheric Rivers in Operational Numerical Weather Prediction Models

2013 ◽  
Vol 28 (6) ◽  
pp. 1337-1352 ◽  
Author(s):  
Gary A. Wick ◽  
Paul J. Neiman ◽  
F. Martin Ralph ◽  
Thomas M. Hamill
Keyword(s):  
Water Vapor ◽  
Lead Time ◽  
Prediction Models ◽  
Weather Prediction ◽  
Time Model ◽  
Atmospheric Rivers ◽  
Position Bias ◽  
Forecast Lead Time ◽  
Northeastern Pacific ◽  
Numerical Weather Prediction Models

Abstract The ability of five operational ensemble forecast systems to accurately represent and predict atmospheric rivers (ARs) is evaluated as a function of lead time out to 10 days over the northeastern Pacific Ocean and west coast of North America. The study employs the recently developed Atmospheric River Detection Tool to compare the distinctive signature of ARs in integrated water vapor (IWV) fields from model forecasts and corresponding satellite-derived observations. The model forecast characteristics evaluated include the prediction of occurrence of ARs, the width of the IWV signature of ARs, their core strength as represented by the IWV content along the AR axis, and the occurrence and location of AR landfall. Analysis of three cool seasons shows that while the overall occurrence of ARs is well forecast out to a 10-day lead, forecasts of landfall occurrence are poorer, and skill degrades with increasing lead time. Average errors in the position of landfall are significant, increasing to over 800 km at 10-day lead time. Also, there is a 1°–2° southward position bias at 7-day lead time. The forecast IWV content along the AR axis possesses a slight moist bias averaged over the entire AR but little bias near landfall. The IWV biases are nearly independent of forecast lead time. Model spatial resolution is a factor in forecast skill and model differences are greatest for forecasts of AR width. This width error is greatest for coarser-resolution models that have positive width biases that increase with forecast lead time.

scholarly journals Improving QPF by blending techniques at the Meteorological Service of Catalonia

2010 ◽  
Vol 10 (7) ◽  
pp. 1443-1455 ◽  
Author(s):  
A. Atencia ◽  
T. Rigo ◽  
A. Sairouni ◽  
J. Moré ◽  
J. Bech ◽  
...  
Keyword(s):  
Lead Time ◽  
Weather Prediction ◽  
Radar Reflectivity ◽  
Precipitation Forecast ◽  
Short Term ◽  
Convective Structures ◽  
Prediction Horizon ◽  
Quantitative Precipitation Forecast ◽  
Correction Technique ◽  
Nwp Model

Abstract. The current operational very short-term and short-term quantitative precipitation forecast (QPF) at the Meteorological Service of Catalonia (SMC) is made by three different methodologies: Advection of the radar reflectivity field (ADV), Identification, tracking and forecasting of convective structures (CST) and numerical weather prediction (NWP) models using observational data assimilation (radar, satellite, etc.). These precipitation forecasts have different characteristics, lead time and spatial resolutions. The objective of this study is to combine these methods in order to obtain a single and optimized QPF at each lead time. This combination (blending) of the radar forecast (ADV and CST) and precipitation forecast from NWP model is carried out by means of different methodologies according to the prediction horizon. Firstly, in order to take advantage of the rainfall location and intensity from radar observations, a phase correction technique is applied to the NWP output to derive an additional corrected forecast (MCO). To select the best precipitation estimation in the first and second hour (t+1 h and t+2 h), the information from radar advection (ADV) and the corrected outputs from the model (MCO) are mixed by using different weights, which vary dynamically, according to indexes that quantify the quality of these predictions. This procedure has the ability to integrate the skill of rainfall location and patterns that are given by the advection of radar reflectivity field with the capacity of generating new precipitation areas from the NWP models. From the third hour (t+3 h), as radar-based forecasting has generally low skills, only the quantitative precipitation forecast from model is used. This blending of different sources of prediction is verified for different types of episodes (convective, moderately convective and stratiform) to obtain a robust methodology for implementing it in an operational and dynamic way.

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