Assessment of atmospheric conditions over the Hong Thai Binh river watershed by means of dynamically downscaled ERA-20C reanalysis data

2018 ◽  
Vol 11 (2) ◽  
pp. 540-555 ◽  
Author(s):  
C. Ho ◽  
A. Nguyen ◽  
A. Ercan ◽  
M. L. Kavvas ◽  
V. Nguyen ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Wrf Model ◽  
Reanalysis Data ◽  
Observation Data ◽  
Human Society ◽  
Atmospheric Conditions ◽  
Study Region ◽  
River Watershed ◽  
Environmentally Sustainable ◽  
Atmospheric Data

Abstract Long-term, high spatial and temporal resolution of atmospheric data is crucial for the purpose of reducing the effects of hydro-meteorological risks on human society in an economically and environmentally sustainable manner. However, such information usually is limited in transboundary regions due to different governmental policies, and to conflicts in the sharing of data. In this study, high spatial and temporal resolution atmospheric data were reconstructed by means of the Weather Research and Forecasting Model-WRF with input provided from the global atmospheric reanalysis of the 20th century (ERA-20C) over the Hong-Thai Binh River watershed (H-TBRW). The WRF model was implemented over the physical boundaries of the study region based on ERA-20C reanalysis data and was configured based on existing ground observation data in Vietnam's territories, and the global Aphrodite precipitation data. With the validated WRF model for H-TBRW, the reconstructed atmospheric data were first reconstructed for 1950–2010, and then were evaluated by time series and spatial analysis methods. The results of this study suggest no significant trend in the annual accumulated precipitation depth, while there were upward trends in annual temperature at both the point and watershed scale. Furthermore, the results confirm that topographic conditions have significant effects on the climatic system such as on precipitation and temperature.

Implementation of a Silver Iodide Cloud-Seeding Parameterization in WRF. Part II: 3D Simulations of Actual Seeding Events and Sensitivity Tests

2013 ◽  
Vol 52 (6) ◽  
pp. 1458-1476 ◽  
Author(s):  
Lulin Xue ◽  
Sarah A. Tessendorf ◽  
Eric Nelson ◽  
Roy Rasmussen ◽  
Daniel Breed ◽  
...  
Keyword(s):  
Silver Iodide ◽  
Winter Season ◽  
Wrf Model ◽  
Reanalysis Data ◽  
Atmospheric Conditions ◽  
Cloud Seeding ◽  
The North ◽  
Cloud Properties ◽  
Regional Reanalysis ◽  
Seeding Effects

AbstractFour cloud-seeding cases over southern Idaho during the 2010/11 winter season have been simulated by the Weather Research and Forecasting (WRF) model using the coupled silver iodide (AgI) cloud-seeding scheme that was described in Part I. The seeding effects of both ground-based and airborne seeding as well as the impacts of model physics, seeding rates, location, timing, and cloud properties on seeding effects have been investigated. The results were compared with those from Part I and showed the following: 1) For the four cases tested in this study, control simulations driven by the Real-Time Four Dimensional Data Assimilation (RTFDDA) WRF forecast data generated more realistic atmospheric conditions and precipitation patterns than those driven by the North America Regional Reanalysis data. Sensitivity experiments therefore used the RTFDDA data. 2) Glaciogenic cloud seeding increased orographic precipitation by less than 1% over the simulation domain, including the Snake River basin, and by up to 5% over the target areas. The local values of the relative precipitation enhancement by seeding were ~20%. Most of the enhancement came from vapor depletion. 3) The seeding effect was inversely related to the natural precipitation efficiency but was positively related to seeding rates. 4) Airborne seeding is generally more efficient than ground-based seeding in terms of targeting, but its efficiency depends on local meteorological conditions. 5) The normalized seeding effects ranged from 0.4 to 1.6 under various conditions for a certain seeding event.

scholarly journals Fusion of Simulated and Observational Temperature Data in the Beijing-Tianjin-Hebei Region Based on High-Accuracy Surface Modeling

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Yu Liu ◽  
Tianxiang Yue ◽  
Yimeng Jiao ◽  
Yapeng Zhao ◽  
Zhengyi Bao
Keyword(s):  
Wrf Model ◽  
Absolute Error ◽  
High Accuracy ◽  
Surface Modeling ◽  
Temperature Fields ◽  
Observation Data ◽  
Human Society ◽  
Fusion Data ◽  
Before And After ◽  
Temperature Records

Temperature changes have a major impact on all aspects of human society and have attracted global attention. The scarcity of observation data and the inaccuracy of the models make obtaining accurate temperature distributions a challenge. This study introduces high-accuracy surface modeling (HASM) combined with temperature simulations from the Weather Research and Forecasting (WRF) model and temperature records from observation stations to investigate the spatiotemporal characteristics of temperature in the Beijing-Tianjin-Hebei region during the period of 1956–2005. Leave-one-out cross-validation is applied to verify the temperature fields before and after the fusion of the models. The results indicate that the WRF model has a limited ability to simulate temperature conditions, but the overall deviation across the region is relatively large. The fusion results of the HASM decrease the mean absolute error (MAE) and the root-mean-square error (RMSE) by half in most instances, and the correlation between the fusion data and observation data is approximately 0.01–0.03 higher than that with the WRF simulation data. Based on the fusion data, obvious warming trends are observed during 1976–2005. In general, temperatures in spring, summer, and autumn increase rapidly from 1996 to 2005 but from 1976 to 1995 in winter. The substantial fluctuations in the interannual temperature during 1996–2005 in summer, autumn, and winter may be related to the frequent occurrence of extreme weather. The spatial distribution of temperature change differs in each season during 1956–1995. A dramatic increase in temperature occurs in the western part of the study area during 1996–2005 but with no seasonal difference.

scholarly journals Development of high-resolution 72 h precipitation and hillslope flood maps over a tropical transboundary region by physically based numerical atmospheric–hydrologic modeling

2020 ◽  
Vol 11 (S1) ◽  
pp. 387-406 ◽  
Author(s):  
T. Trinh ◽  
C. Ho ◽  
N. Do ◽  
A. Ercan ◽  
M. L. Kavvas
Keyword(s):  
High Resolution ◽  
Water Resource Management ◽  
Wrf Model ◽  
Heavy Precipitation ◽  
Reanalysis Data ◽  
Observation Data ◽  
Extreme Flood ◽  
Hydrologic Data ◽  
Flood Maps

Abstract Long-term, high spatial and temporal resolution atmospheric and hydrologic data are crucial for water resource management. However, reliable high-quality precipitation and hydrologic data are not available in various regions around the world. This is, in particular, the case in transboundary regions, which have no formal data sharing agreement among countries. This study introduces an approach to construct long-term high-resolution extreme 72 h precipitation and hillslope flood maps over a tropical transboundary region by the coupled physical hydroclimate WEHY-WRF model. For the case study, Da and Thao River watersheds (D-TRW), within Vietnam and China, were selected. The WEHY-WRF model was set up over the target region based on ERA-20C reanalysis data and was calibrated based on existing ground observation data. After successfully configuring, WEHY-WRF is able to produce hourly atmospheric and hydrologic conditions at fine resolution over the target watersheds during 1900–2010. From the modeled 72 h precipitation and flood events, it can be seen that the main precipitation mechanism of DRW and TRW are both the summer monsoon and tropical cyclone. In addition, it can be concluded that heavy precipitation may not be the only reason to create an extreme flood event. The effects of topography, soil, and land use/cover also need to be considered in such nonlinear atmospheric and hydrologic processes. Last but not least, the long-term high-resolution extreme 72 h precipitation and hillslope flood maps over a tropical transboundary region, D-TRW, were constructed based on 111 largest annual historical events during 1900–2010.

scholarly journals An Improved Method Combining CNN and 1D-Var for the Retrieval of Atmospheric Humidity Profiles from FY-4A/GIIRS Hyperspectral Data

2021 ◽  
Vol 13 (23) ◽  
pp. 4737
Author(s):  
Pengyu Huang ◽  
Qiang Guo ◽  
Changpei Han ◽  
Huangwei Tu ◽  
Chunming Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Brightness Temperature ◽  
Temporal Resolution ◽  
Reanalysis Data ◽  
Temperature Data ◽  
High Temporal Resolution ◽  
Observation Data ◽  
Retrieval Method ◽  
Humidity Profile ◽  
Humidity Profiles

FY-4A/GIIRS (Geosynchronous Interferometric Infrared Sounder) is the first infrared hyperspectral atmospheric vertical detector in geostationary orbit. Compared to other similar instruments, it has the advantages of high temporal resolution and stationary relative to the ground. Based on the characteristics of GIIRS observation data, we proposed a humidity profile retrieval method. We fully utilized the information provided by the observation and forecast data, and used the two-dimensional brightness temperature data with the dimension of time and optical spectrum as the input of the CNN (convolution neural network model). Then, the obtained brightness temperature data were shown to be more suitable as the input for the physical retrieval method for humidity than the conventional correction method, improving the accuracy of humidity profile retrieval. We performed two comparative experiments. The first experiment results indicate that, compared to ordinary linear correction and ANN (artificial neural network algorithm) correction, our revised observed brightness temperature data are much closer to the simulated brightness temperature obtained by inputting ERA5 reanalysis data into RTTOV (Radiative Transfer for TOVS). The results of the second experiment indicate that the accuracy of the humidity profile retrieved by our method is higher than that of conventional ANN and 1D-Var (one-dimensional variational algorithm). With ERA5 reanalysis data as the reference value, the RMSE (Root Mean Squared Error) of the humidity profiles by our method is less than 8.2% between 250 and 600 hPa. Our method holds the unique advantage of the high temporal resolution of GIIRS, improves the accuracy of humidity profile retrieval, and proves that the combination of machine learning and the physical method is a compelling idea in the field of satellite atmospheric remote sensing worthy of further exploration.

The Three-Cornered Hat Method for Estimating Error Variances of Three or More Atmospheric Data Sets – Part II: Evaluating Radio Occultation and Radiosonde Observations, Global Model Forecasts, and Reanalyses

2021 ◽  
Author(s):  
Therese Rieckh ◽  
Jeremiah P. Sjoberg ◽  
Richard A. Anthes
Keyword(s):  
Radio Occultation ◽  
State Of The Art ◽  
Specific Humidity ◽  
Data Sets ◽  
Error Growth ◽  
Atmospheric Conditions ◽  
Error Statistics ◽  
Weather And Climate ◽  
Atmospheric Data ◽  
The Impact

AbstractWe apply the three-cornered hat (3CH) method to estimate refractivity, bending angle, and specific humidity error variances for a number of data sets widely used in research and/or operations: radiosondes, radio occultation (COSMIC, COSMIC-2), NCEP global forecasts, and nine reanalyses. We use a large number and combinations of data sets to obtain insights into the impact of the error correlations among different data sets that affect 3CH estimates. Error correlations may be caused by actual correlations of errors, representativeness differences, or imperfect co-location of the data sets. We show that the 3CH method discriminates among the data sets and how error statistics of observations compare to state-of-the-art reanalyses and forecasts, as well as reanalyses that do not assimilate satellite data. We explore results for October and November 2006 and 2019 over different latitudinal regions and show error growth of the NCEP forecasts with time. Because of the importance of tropospheric water vapor to weather and climate, we compare error estimates of refractivity for dry and moist atmospheric conditions.

scholarly journals Empirical atmospheric thresholds for debris flows and flash floods in the southern French Alps

2014 ◽  
Vol 14 (6) ◽  
pp. 1517-1530 ◽  
Author(s):  
T. Turkington ◽  
J. Ettema ◽  
C. J. van Westen ◽  
K. Breinl
Keyword(s):  
Debris Flows ◽  
Warning System ◽  
Flash Floods ◽  
Rain Gauge ◽  
Reanalysis Data ◽  
Specific Humidity ◽  
Atmospheric Conditions ◽  
Convective Rainfall ◽  
French Alps ◽  
Mountainous Regions

Abstract. Debris flows and flash floods are often preceded by intense, convective rainfall. The establishment of reliable rainfall thresholds is an important component for quantitative hazard and risk assessment, and for the development of an early warning system. Traditional empirical thresholds based on peak intensity, duration and antecedent rainfall can be difficult to verify due to the localized character of the rainfall and the absence of weather radar or sufficiently dense rain gauge networks in mountainous regions. However, convective rainfall can be strongly linked to regional atmospheric patterns and profiles. There is potential to employ this in empirical threshold analysis. This work develops a methodology to determine robust thresholds for flash floods and debris flows utilizing regional atmospheric conditions derived from ECMWF ERA-Interim reanalysis data, comparing the results with rain-gauge-derived thresholds. The method includes selecting the appropriate atmospheric indicators, categorizing the potential thresholds, determining and testing the thresholds. The method is tested in the Ubaye Valley in the southern French Alps (548 km2), which is known to have localized convection triggered debris flows and flash floods. This paper shows that instability of the atmosphere and specific humidity at 700 hPa are the most important atmospheric indicators for debris flows and flash floods in the study area. Furthermore, this paper demonstrates that atmospheric reanalysis data are an important asset, and could replace rainfall measurements in empirical exceedance thresholds for debris flows and flash floods.

scholarly journals Optimal Estimation MSG-SEVIRI Clear-Sky Total Column Water Vapour Retrieval Using the Split Window Difference

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1256
Author(s):  
Jan El Kassar ◽  
Cintia Carbajal Henken ◽  
Rene Preusker ◽  
Jürgen Fischer
Keyword(s):  
Water Vapour ◽  
Large Scale ◽  
Pearson Correlation ◽  
Microwave Radiometer ◽  
Optimal Estimation ◽  
Forward Model ◽  
Observation Data ◽  
Atmospheric Conditions ◽  
Uncertainty Estimates ◽  
Total Column

A new algorithm for the retrieval of day-time total column water vapour (TCWV) from measurements of a MSG-SEVIRI (Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager) instrument is presented. The retrieval is based on a forward operator, at the core of which lies Radiative Transfer for TIROS Operational Vertical Sounder (RTTOV). This forward model relates TCWV and surface temperature to brightness temperatures in the split window at 11 and 12µm with the use of a first guess for temperature and humidity profiles from the ERA5 reanalysis. The forward model is then embedded in a full Optimal Estimation (OE) method, which yields pixel by pixel uncertainty estimates and performance indicators. The algorithm is applicable to any instrument which features the split window configuration, given a first guess for atmospheric conditions (i.e., from NWP) and an estimate of surface emissivity at 11 µm. The algorithm was developed within the framework of RealPEP (Near-Realtime Quantitative Precipitation Estimation and Prediction) in which the advancement of the estimation and nowcasting of extreme precipitation and flooding in Germany are studied. Thus, processing and validation has been limited to the German domain. Three independent ground-based TCWV observation data sets were used as reference, i.e., AERONET (Aerosol Robotic Network), GNSS Germany (Global Navigation Satellite System) and measurements from two MWR (Microwave Radiometer) sites. The validation concludes with good agreement, with absolute biases between 0.11 and 2.85 kg/m2, root mean square deviations (rmsds) between 1.63 and 3.24 kg/m2 and Pearson correlation coefficients ranging from 0.96 to 0.98. The retrievals uncertainty estimates were evaluated against AERONET. The comparison suggests that, in sum, uncertainties are estimated well, while still some error sources seem to be over- and underestimated. In limited case studies it could be shown that SEVIRI TCWV is capable to both display large scale variabilities in water vapour fields and reproduce the daily course of water vapour exposed by ground-based observations.

scholarly journals Meteorology in field artillery ballistic calculations

2021 ◽  
Vol 2090 (1) ◽  
pp. 012149
Author(s):  
M Mendel
Keyword(s):  
Atmospheric Pressure ◽  
Open Space ◽  
Meteorological Parameters ◽  
Meteorological Data ◽  
Armed Forces ◽  
Weather Conditions ◽  
Atmospheric Conditions ◽  
Area Of Interest ◽  
Single Probe ◽  
Atmospheric Data

Abstract The most important meteorological data are:ambient temperature, precipitation quantity, air humidity, amount and type of clouds, atmospheric pressure, wind direction and speed, visibility, weather phenomena. These coefficients impact the effectiveness of various combat activities, especially those conducted in an open space. Knowledge of future weather conditions is essential for planning the location, calculating times, choice of means, and other aspects relevant to the upcoming operations. Taking weather conditions into account is vital, specifically when it comes to planning combat operations, where the accuracy in cooperation is of paramount importance. Rocket forces and artillery is a particular type of armed forces where weather conditions are critical. The effectiveness of artillery depends on ballistic calculation precision, and so knowledge of atmospheric conditions is fundamental. Atmospheric data are collected from sounding using a single probe attached to a balloon. It is generally known that particular meteorological parameters change in a smooth spatial manner depending on various coefficients. Information about the atmosphere collected by a single probe may be insufficient, due to the possibility of a balloon drifting away from the area of interest, and the calculations are based on data received from its probe. In this paper, I will suggest a method for preparing artillery use meteorologically, which takes into account the distribution of particular meteorological coefficients over a given area.

scholarly journals Simulation of Severe Thunderstorms in Conjugation of Westerly and Easterly on 31 March 2019 in Bangladesh

2022 ◽  
Vol 12 (3) ◽  
pp. 29-43
Author(s):  
Samarendra Karmakar ◽  
Mohan Kumar Das ◽  
Md Quamrul Hassam ◽  
Md Abdul Mannan
Keyword(s):  
South Asian ◽  
Wrf Model ◽  
Horizontal Resolution ◽  
Weather Research And Forecasting ◽  
Atmospheric Conditions ◽  
Microphysics Scheme ◽  
Severe Thunderstorms ◽  
Synoptic Conditions ◽  
Pbl Scheme ◽  
Single Moment

The diagnostic and prognostic studies of thunderstorms/squalls are very important to save live and loss of properties. The present study aims at diagnose the different tropospheric parameters, instability and synoptic conditions associated the severe thunderstorms with squalls, which occurred at different places in Bangladesh on 31 March 2019. For prognostic purposes, the severe thunderstorms occurred on 31 March 2019 have been numerically simulated. In this regard, the Weather Research and Forecasting (WRF) model is used to predict different atmospheric conditions associated with the severe storms. The study domain is selected for 9 km horizontal resolution, which almost covers the south Asian region. Numerical experiments have been conducted with the combination of WRF single-moment 6 class (WSM6) microphysics scheme with Yonsei University (YSU) PBL scheme in simulation of the squall events. Model simulated results are compared with the available observations. The observed values of CAPE at Kolkata both at 0000 and 1200 UTC were 2680.4 and 3039.9 J kg-1 respectively on 31 March 2019 and are found to be comparable with the simulated values. The area averaged actual rainfall for 24 hrs is found is 22.4 mm, which complies with the simulated rainfall of 20-25 mm for 24 hrs. Journal of Engineering Science 12(3), 2021, 29-43

scholarly journals Assessing the Potential of a Long-Term Climate Forecast for Cuba Using the WRF Model

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Lourdes Álvarez-Escudero ◽  
Yandy G. Mayor ◽  
Israel Borrajero-Montejo ◽  
Arnoldo Bezanilla-Morlot
Keyword(s):  
Mesoscale Model ◽  
Initial Conditions ◽  
Wrf Model ◽  
Reanalysis Data ◽  
Weather Research And Forecasting ◽  
Tropical Storms ◽  
Precipitation Regime ◽  
Wide Debate ◽  
One Year ◽  
Cumulative Precipitation

Seasonal climatic prediction studies are a matter of wide debate all over the world. Cuba, a mainly agricultural nation, should greatly benefit from the knowledge, which is available months in advance of the precipitation regime and allows for the proper management of water resources. In this work, a series of six experiments were made with a mesoscale model WRF (Weather Research and Forecasting Model) that produced a 15-month forecast for each month of cumulative precipitation starting at two dates, and for three non-consecutive years with different meteorological characteristics: one dry year (2004), one year that started dry and turned rainy (2005), and one year where several tropical storms occurred (2008). ERA-Interim reanalysis data were used for the initial and border conditions and experiments started 1 month before the beginning of the rainy and the dry seasons, respectively. In a general sense, the experience of using WRF indicated that it was a valid resource for seasonal forecast, since the results obtained were in the same range as those reported by the literature for similar cases. Several limitations were revealed by the results: the forecasts underestimated the monthly cumulative precipitation figures, tropical storms entering through the borders sometimes followed courses different from the real courses inside the working domain, storms that developed inside the domain were not reproduced by WRF, and differences in initial conditions led to significantly different forecasts for the corresponding time steps (nonlinearity). Changing the model parameterizations and initial conditions of the ensemble forecast experiments was recommended.

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