scholarly journals Case Studies of Low‐Visibility Forecasting in Falling Snow With WRF Model

2017 ◽  
Vol 122 (23) ◽  
Author(s):  
Haibo Huang ◽  
Guangxing Zhang
Keyword(s):  
Case Studies ◽  
Wrf Model ◽  
Low Visibility ◽  
Visibility Forecasting

scholarly journals Biogenic isoprene emissions driven by regional weather predictions using different initialization methods: case studies during the SEAC<sup>4</sup>RS and DISCOVER-AQ airborne campaigns

2017 ◽  
Vol 10 (8) ◽  
pp. 3085-3104 ◽  
Author(s):  
Min Huang ◽  
Gregory R. Carmichael ◽  
James H. Crawford ◽  
Armin Wisthaler ◽  
Xiwu Zhan ◽  
...  
Keyword(s):  
Case Studies ◽  
Air Temperature ◽  
Initial Conditions ◽  
Wrf Model ◽  
Surface Air Temperature ◽  
Heat Fluxes ◽  
Aircraft Observation ◽  
Near Surface ◽  
Boundary Layer Height ◽  
Small Scales

Abstract. Land and atmospheric initial conditions of the Weather Research and Forecasting (WRF) model are often interpolated from a different model output. We perform case studies during NASA's SEAC4RS and DISCOVER-AQ Houston airborne campaigns, demonstrating that using land initial conditions directly downscaled from a coarser resolution dataset led to significant positive biases in the coupled NASA-Unified WRF (NUWRF, version 7) surface and near-surface air temperature and planetary boundary layer height (PBLH) around the Missouri Ozarks and Houston, Texas, as well as poorly partitioned latent and sensible heat fluxes. Replacing land initial conditions with the output from a long-term offline Land Information System (LIS) simulation can effectively reduce the positive biases in NUWRF surface air temperature by ∼ 2 °C. We also show that the LIS land initialization can modify surface air temperature errors almost 10 times as effectively as applying a different atmospheric initialization method. The LIS-NUWRF-based isoprene emission calculations by the Model of Emissions of Gases and Aerosols from Nature (MEGAN, version 2.1) are at least 20 % lower than those computed using the coarser resolution data-initialized NUWRF run, and are closer to aircraft-observation-derived emissions. Higher resolution MEGAN calculations are prone to amplified discrepancies with aircraft-observation-derived emissions on small scales. This is possibly a result of some limitations of MEGAN's parameterization and uncertainty in its inputs on small scales, as well as the representation error and the neglect of horizontal transport in deriving emissions from aircraft data. This study emphasizes the importance of proper land initialization to the coupled atmospheric weather modeling and the follow-on emission modeling. We anticipate it to also be critical to accurately representing other processes included in air quality modeling and chemical data assimilation. Having more confidence in the weather inputs is also beneficial for determining and quantifying the other sources of uncertainties (e.g., parameterization, other input data) of the models that they drive.

Operational Precipitation Forecast Over China Using the Weather Research and Forecasting (WRF) Model at a Gray-Zone Resolution: Impact of Convection Parameterization

2021 ◽  
Author(s):  
XU ZHANG ◽  
YUHUA YANG ◽  
BAODE CHEN ◽  
WEI HUANG
Keyword(s):  
Case Studies ◽  
Southern China ◽  
Wrf Model ◽  
Warm Season ◽  
Precipitation Forecast ◽  
Weather Research And Forecasting ◽  
Operational System ◽  
Quantitative Precipitation Forecast ◽  
Geographical Regions ◽  
Convection Parameterization

AbstractThe quantitative precipitation forecast in the 9 km operational modeling system (without the use of a convection parameterization scheme) at the Shanghai Meteorological Service (SMS) usually suffers from excessive precipitation at the grid scale and less-structured precipitation patterns. Two scale-aware convection parameterizations were tested in the operational system to mitigate these deficiencies. Their impacts on the warm-season precipitation forecast over China were analyzed in case studies and two-month retrospective forecasts. The results from case studies show that the importance of convection parameterization depends on geographical regions and weather regimes. Considering a proper magnitude of parameterized convection can produce more realistic precipitation distribution and reduce excessive grid-scale precipitation in southern China. In the northeast and southwest China, however, the convection parameterization plays an insignificant role in precipitation forecast because of strong synoptic-scale forcing. A statistical evaluation of the two-month retrospective forecasts indicates that the forecast skill for precipitation in the 9-km operational system is improved by choosing proper convection parameterization. This study suggests that improvement in contemporary convection parameterizations is needed for their usage for various meteorological conditions and reasonable partitioning between parameterized and resolved convection.

scholarly journals WRF Hindcasts of Cold Front Passages over the ARM Eastern North Atlantic Site: A Sensitivity Study

2018 ◽  
Vol 146 (8) ◽  
pp. 2417-2432 ◽  
Author(s):  
Fayçal Lamraoui ◽  
James F. Booth ◽  
Catherine M. Naud
Keyword(s):  
Case Studies ◽  
North Atlantic ◽  
Cold Front ◽  
Domain Boundary ◽  
Wrf Model ◽  
Domain Size ◽  
Sensitivity Study ◽  
Spectral Nudging ◽  
Eastern North Atlantic ◽  
The Impact

Abstract The present study explores the ability of the Weather Research and Forecasting (WRF) Model to accurately reproduce the passage of extratropical cold fronts at the DOE ARM eastern North Atlantic (ENA) observation site on the Azores. An analysis of three case studies is performed in which the impact of the WRF domain size, position of the model boundary relative to the ENA site, grid spacing, and spectral nudging conditions are explored. The results from these case studies indicate that model biases in the timing and duration of cold front passages change with the distance between the model domain boundary and the ENA site. For these three cases, if the western model boundary is farther than 1500 km from the site, the front becomes too meridional and fails to reach the site, making 1000 or 1500 km the optimal distances. In contrast, integrations with small distances (e.g., 500 km) between the site and domain boundaries have inadequate spatial spinup (i.e., the domain is too small for the model to properly stabilize). For all three cases, regardless of domain size, the model has biases in its upper-level circulation that impact the position and timing of the front. However, this issue is most serious for 4000-km2 domains and larger. For these domains, prolonged spectral nudging can correct cold front biases. As such, this analysis provides a framework to optimize the WRF Model configuration necessary for a realistic hindcast of a cold front passage at a fixed location centered in a domain as large as computationally possible.

scholarly journals Biomass Burning Aerosols and the Low Visibility Events in Southeast Asia

2016 ◽  
Author(s):  
Hsiang-He Lee ◽  
Rotem Z. Bar-Or ◽  
Chien Wang
Keyword(s):  
Particulate Matter ◽  
Southeast Asia ◽  
Biomass Burning ◽  
Wrf Model ◽  
Weather Conditions ◽  
General Situation ◽  
Kuala Lumpur ◽  
The Past ◽  
Low Visibility ◽  
Aerosol Module

Abstract. Fires including peatland burning in Southeast Asia have become a major concern of general public as well as governments in the region. This is because that aerosols emitted from such fires can cause persistent haze events under favorite weather conditions in downwind locations, degrading visibility and causing human health issues. In order to improve our understanding of the spatial-temporal coverage and influence of biomass burning aerosols in Southeast Asia, we have used surface visibility and particulate matter concentration observations, added by decadal long (2002 to 2014) simulations using the Weather Research and Forecasting (WRF) model with a fire aerosol module, driven by high-resolution biomass burning emission inventories. We find that in the past decade, fire aerosols are responsible for nearly all the events with very low visibility (< 7 km), and a substantial fraction of the low visibility events (visibility < 10 km) in the major metropolitan areas of Southeast Asia: 38 % in Bangkok, 35 % in Kuala Lumpur, and 34 % in Singapore. Biomass burnings in Mainland Southeast Asia account for the largest contributor to total fire produced PM2.5 in Bangkok (99.1 %), while biomass burning in Sumatra is the major contributor to fire produced PM2.5 in Kuala Lumpur (49 %) and Singapore (41 %). To examine the general situation across the region, we have further defined and derived a new integrated metric for 50 cities of the Association of Southeast Asian Nations, i.e., Haze Exposure Days (HEDs) that measures the annual exposure days of these cities to low visibility (< 10 km) caused by particulate matter pollution. It is shown that HEDs have increased steadily in the past decade across cities with both high and low populations. Fire events are found to be responsible for about half of the total HEDs. Therefore, our result suggests that in order to improve the overall air quality in Southeast Asia, mitigation policies targeting at both biomass and fossil fuel burning sources need to be put in effect.

Climatological Tools for Low Visibility Forecasting

2007 ◽  
Vol 164 (6-7) ◽  
pp. 1383-1396 ◽  
Author(s):  
Otto Hyvärinen ◽  
Jukka Julkunen ◽  
Vesa Nietosvaara
Keyword(s):  
Low Visibility ◽  
Visibility Forecasting

scholarly journals Research on the usability of different machine learning methods in visibility forecasting

2021 ◽  
Author(s):  
Pak Wai Chan ◽  
Wu Wen ◽  
Lei Li
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Percentage Error ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Haze Pollution ◽  
Low Visibility ◽  
Visibility Forecasting

Haze pollution, mainly characterized by low visibility, is one of the main environmental problems currently faced by China. Accurate haze forecasts facilitate the implementation of preventive measures to control the emission of air pollutants and, thereby mitigate haze pollution. However, it is not easy to accurately predict the low visibility events induced by haze, which requires not only accurate prediction for weather elements, but also refined and real-time updated source emission inventory. In order to obtain reliable forecasting tools, this paper studies the usability of several popular machine learning methods, such as support vector machine, k-nearest neighbor, random forest, as well as several deep learning methods, on the visibility forecasting. Starting from the main factors related to visibility, the relationships between wind speed, wind direction, temperature, humidity, and visibility are discussed. Training and forecasting were performed using the machine learning methods. The accuracy of these methods in visibility forecasting was confirmed through several parameters (i.e., root-mean-square error, mean absolute error, and mean absolute percentage error). The results show that: (1) Among all meteorological parameters, wind speed was the best at reflecting the visibility change patterns; (2) RNN LSTM, and GRU methods performs almost equally well on short-term visibility forecasts(i.e. 1h, 3h, and 6h); (3) A classical machine learning method (i.e. the SVM) performs well in mid- and long-term visibility forecasts; (4) The machine learning methods also have a certain degree of forecast accuracy even for long time periods (e.g. of 72h).

scholarly journals Real-time mesoscale modeling for short range prediction of weather over Maitri region in Antarctica

MAUSAM ◽  
2021 ◽  
Vol 62 (4) ◽  
pp. 535-546
Author(s):  
S.K.ROY BHOWMIK ◽  
ANUPAM KUMAR ◽  
ANANDA K.DAS
Keyword(s):  
Real Time ◽  
Case Studies ◽  
Sea Level ◽  
Short Range ◽  
Wrf Model ◽  
India Meteorological Department ◽  
Horizontal Resolution ◽  
Sea Level Pressure ◽  
Mean Sea Level ◽  
Level Pressure

The main objective of this paper is to implement Polar WRF model for the Maitri (Lat. 70° 45 S, Long. 11° 44 E) region at the horizontal resolution of 15 km using initial and boundary conditions of the Global Forecast System T-382 operational at the India Meteorological Department (IMD). The study evaluates the performance of the model using the conventional approach of case studies. The results of the case studies illustrated in this paper reveal that the model is capable of capturing synoptic and meso-scale weather systems. Forecast fields are consistent with the corresponding analysis fields. Synoptic charts of mean sea level pressure prepared by the Weather Service of South Africa at Pretoria are used for the model validation. The model derived meteograms of mean sea level pressure are compared against the corresponding observations. The study demonstrates the usefulness of the forecast products for short range forecasting of weather over the Maitri region. The forecast outputs are made available in the real-time mode in the national web site of IMD www.imd.gov.in. The study is expected to benefit weather forecasters at Maitri.

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