scholarly journals Forecasting mesoscale precipitation using the MM5 model with the four-dimensional data assimilation (FDDA) technique

2013 ◽  
Vol 7 (3) ◽  
pp. 258-263
Keyword(s):  
Data Assimilation ◽  
Mesoscale Model ◽  
System Development ◽  
Time Integration ◽  
Mountain Region ◽  
Precipitation Event ◽  
Model Data ◽  
Interval Model ◽  
Mm5 Model ◽  
Polar Orbiting Satellite

The two basic forms of multi-scale data assimilation procedures (FDDA), based on Newtonian relaxation, of analysis and observations nudging have been applied for precipitation event period occurred over Portugal during summer season, using the Fifth Generation Mesoscale Model (MM5) developed and maintained by the Pennsylvania State University and National Center for Atmospheric Research (PSU/NCAR). The model has been configured for three nested grid domains covering part of the Eastern part of North Atlantic region evolving the Portugal, with 35 vertical levels, from surface up to 100 hPa top level. The model forecasting have been conducted employing daily available data from surface observational network, radio-sounding from Lisbon/Portugal and NOAA-16 polar orbiting satellite retrieved vertical profiles data. The three integration domains of MM5 model have been processed using, as boundary and first guess fields, the global atmospheric forecast NCEP-NWS/AVN model data gathered through the Unidata Local Data Manager (LDM)/Unidata Internet Data Distribution (IDD) system. All daily forecasting, with FDDA and with no FDDA, have been run for 60 hours forecast, with 30 minutes interval model data output to provide enough timely detailed results. The FDDA analysis presented a quite reasonable data ingesting volume of almost all available satellite data, with the exception of humidity data retrieved for high levels, above around 500 hPa. The obtained results indicate that, even using weak FDDA constraint coefficient values, presents a significant improvement in the numerical prognosis in the precipitation field, on both space and time integration levels. The results also presented an enhancement of the physics of the convective mesoscale system development, particularly over mountain region, indicating that it would be interesting to conduct an experiment with a dense data collecting platform coverage focused on events which occur in some prevailing mountain region of Portugal.

scholarly journals Automatic transport model data assimilation in Laplace space

2012 ◽  
Vol 48 (1) ◽  
Author(s):  
K. S. Barnhart ◽  
T. H. Illangasekare
Keyword(s):  
Data Assimilation ◽  
Transport Model ◽  
Model Data

Thermally Driven Diurnally Periodic Wind Signals off the East Coast of China

2015 ◽  
Vol 72 (7) ◽  
pp. 2806-2821 ◽  
Author(s):  
Yu Du ◽  
Richard Rotunno
Keyword(s):  
Linear Theory ◽  
East Coast ◽  
Mesoscale Model ◽  
Maximum Amplitude ◽  
Sea Breeze ◽  
Model Data ◽  
Absolute Time ◽  
Thermally Driven ◽  
Diurnal Wind ◽  
Northeast Coast

The characteristics of thermally driven diurnally periodic wind signals off the east coast of China are studied using hourly model data for the period June 2006–11 simulated with a mesoscale model. Analysis of these model data indicates low-level diurnally periodic wind signals propagate eastward off the southeast coast, whereas diurnal wind variations off the northeast coast are nearly in phase. It is found that a simple 2D linear land–sea-breeze model with friction can capture this main difference in propagation character with respect to latitude. Idealized simulations using a simplified version of the mesoscale model that includes surface heating and terrain are found to explain certain features not captured by the present linear theory such as the absolute time phase and cross-coast location of the maximum amplitude of the diurnally periodic winds.

scholarly journals Multiscale Mountain Waves Influencing a Major Orographic Precipitation Event

2007 ◽  
Vol 64 (3) ◽  
pp. 711-737 ◽  
Author(s):  
Matthew F. Garvert ◽  
Bradley Smull ◽  
Cliff Mass
Keyword(s):  
Mesoscale Model ◽  
Precipitation Forecast ◽  
Precipitation Event ◽  
Small Scale ◽  
Second Phase ◽  
Mountain Waves ◽  
Low Level ◽  
Orographic Rainfall ◽  
Vertically Propagating ◽  
Horizontal Wavelength

Abstract This study combines high-resolution mesoscale model simulations and comprehensive airborne Doppler radar observations to identify kinematic structures influencing the production and mesoscale distribution of precipitation and microphysical processes during a period of heavy prefrontal orographic rainfall over the Cascade Mountains of Oregon on 13–14 December 2001 during the second phase of the Improvement of Microphysical Parameterization through Observational Verification Experiment (IMPROVE-2) field program. Airborne-based radar detection of precipitation from well upstream of the Cascades to the lee allows a depiction of terrain-induced wave motions in unprecedented detail. Two distinct scales of mesoscale wave–like air motions are identified: 1) a vertically propagating mountain wave anchored to the Cascade crest associated with strong midlevel zonal (i.e., cross barrier) flow, and 2) smaller-scale (<20-km horizontal wavelength) undulations over the windward foothills triggered by interaction of the low-level along-barrier flow with multiple ridge–valley corrugations oriented perpendicular to the Cascade crest. These undulations modulate cloud liquid water (CLW) and snow mixing ratios in the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5), with modeled structures comparing favorably to radar-documented zones of enhanced reflectivity and CLW measured by the NOAA P3 aircraft. Errors in the model representation of a low-level shear layer and the vertically propagating mountain waves are analyzed through a variety of sensitivity tests, which indicated that the mountain wave’s amplitude and placement are extremely sensitive to the planetary boundary layer (PBL) parameterization being employed. The effects of 1) using unsmoothed versus smoothed terrain and 2) the removal of upstream coastal terrain on the flow and precipitation over the Cascades are evaluated through a series of sensitivity experiments. Inclusion of unsmoothed terrain resulted in net surface precipitation increases of ∼4%–14% over the windward slopes relative to the smoothed-terrain simulation. Small-scale waves (<20-km horizontal wavelength) over the windward slopes significantly impact the horizontal pattern of precipitation and hence quantitative precipitation forecast (QPF) accuracy.

TOMOREF operator as a tool to improve weather forecasts

2021 ◽  
Author(s):  
Natalia Hanna ◽  
Estera Trzcina ◽  
Maciej Kryza ◽  
Witold Rohm
Keyword(s):  
Data Assimilation ◽  
Weather Forecasting ◽  
Positive Impact ◽  
Precipitation Event ◽  
Initial State ◽  
Total Delay ◽  
Numerical Weather ◽  
Numerical Weather Model ◽  
Weather Model ◽  
Tomography Data

<p>The numerical weather model starts from the initial state of the Earth's atmosphere in a given place and time. The initial state is created by blending the previous forecast runs (first-guess), together with observations from different platforms. The better the initial state, the better the forecast; hence, it is worthy to combine new observation types. The GNSS tomography technique, developed in recent years, provides a 3-D field of humidity in the troposphere. This technique shows positive results in the monitoring of severe weather events. However, to assimilate the tomographic outputs to the numerical weather model, the proper observation operator needs to be built.</p><p>This study demonstrates the TOMOREF operator dedicated to the assimilation of the GNSS tomography‐derived 3‐D fields of wet refractivity in a Weather Research and Forecasting (WRF) Data Assimilation (DA) system. The new tool has been tested based on wet refractivity fields derived during a very intense precipitation event. The results were validated using radiosonde observations, synoptic data, ERA5 reanalysis, and radar data. In the presented experiment, a positive impact of the GNSS tomography data assimilation on the forecast of relative humidity (RH) was noticed (an improvement of root‐mean‐square error up to 0.5%). Moreover, within 1 hour after assimilation, the GNSS data reduced the bias of precipitation up to 0.1 mm. Additionally, the assimilation of GNSS tomography data had more influence on the WRF model than the Zenith Total Delay (ZTD) observations, which confirms the potential of the GNSS tomography data for weather forecasting.</p>

scholarly journals Impact of Four-Dimensional Variational Data Assimilation of Atmospheric Motion Vectors on Tropical Cyclone Track Forecasts

2006 ◽  
Vol 21 (4) ◽  
pp. 663-669 ◽  
Author(s):  
Dongliang Wang ◽  
Xudong Liang ◽  
Yihong Duan ◽  
Johnny C. L. Chan
Keyword(s):  
Tropical Cyclone ◽  
Data Assimilation ◽  
Mesoscale Model ◽  
State University ◽  
Motion Vectors ◽  
Geostationary Meteorological Satellite ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors ◽  
Data Assimilation Technique ◽  
The Impact

Abstract The fifth-generation Pennsylvania State University–National Center for Atmospheric Research nonhydrostatic Mesoscale Model is employed to evaluate the impact of the Geostationary Meteorological Satellite-5 water vapor and infrared atmospheric motion vectors (AMVs), incorporated with the four-dimensional variational (4DVAR) data assimilation technique, on tropical cyclone (TC) track predictions. Twenty-two cases from eight different TCs over the western North Pacific in 2002 have been examined. The 4DVAR assimilation of these satellite-derived wind observations leads to appreciable improvements in the track forecasts, with average reductions in track error of ∼5% at 12 h, 12% at 24 h, 10% at 36 h, and 7% at 48 h. Preliminary results suggest that the improvement depends on the quantity of the AMV data available for assimilation.

scholarly journals Impact of the assimilation of lightning data on the precipitation forecast at different forecast ranges

2017 ◽  
Vol 14 ◽  
pp. 187-194 ◽  
Author(s):  
Stefano Federico ◽  
Marco Petracca ◽  
Giulia Panegrossi ◽  
Claudio Transerici ◽  
Stefano Dietrich
Keyword(s):  
Data Assimilation ◽  
Mesoscale Model ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Probability Of Detection ◽  
Precipitation Forecast ◽  
Time Range ◽  
False Alarms ◽  
Forecast Time ◽  
The Impact

Abstract. This study investigates the impact of the assimilation of total lightning data on the precipitation forecast of a numerical weather prediction (NWP) model. The impact of the lightning data assimilation, which uses water vapour substitution, is investigated at different forecast time ranges, namely 3, 6, 12, and 24 h, to determine how long and to what extent the assimilation affects the precipitation forecast of long lasting rainfall events (> 24 h). The methodology developed in a previous study is slightly modified here, and is applied to twenty case studies occurred over Italy by a mesoscale model run at convection-permitting horizontal resolution (4 km). The performance is quantified by dichotomous statistical scores computed using a dense raingauge network over Italy. Results show the important impact of the lightning assimilation on the precipitation forecast, especially for the 3 and 6 h forecast. The probability of detection (POD), for example, increases by 10 % for the 3 h forecast using the assimilation of lightning data compared to the simulation without lightning assimilation for all precipitation thresholds considered. The Equitable Threat Score (ETS) is also improved by the lightning assimilation, especially for thresholds below 40 mm day−1. Results show that the forecast time range is very important because the performance decreases steadily and substantially with the forecast time. The POD, for example, is improved by 1–2 % for the 24 h forecast using lightning data assimilation compared to 10 % of the 3 h forecast. The impact of the false alarms on the model performance is also evidenced by this study.

scholarly journals Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems

2017 ◽  
Author(s):  
Marko Scholze ◽  
Michael Buchwitz ◽  
Wouter Dorigo ◽  
Luis Guanter ◽  
Shaun Quegan
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Global Carbon Cycle ◽  
Full Description ◽  
Model Data ◽  
Terrestrial Carbon ◽  
Carbon Cycle Model ◽  
Terrestrial Carbon Cycle ◽  
Uncertainty Estimates ◽  
Global Carbon

Abstract. The global carbon cycle is an important component of the Earth system and it interacts with the hydrological, energy and nutrient cycles as well as ecosystem dynamics. A better understanding of the global carbon cycle is required for improved projections of climate change including corresponding changes in water and food resources and for the verification 5 of measures to reduce anthropogenic greenhouse gas emissions. An improved understanding of the carbon cycle can be achieved by model-data fusion or data assimilation systems, which integrate observations relevant to the carbon cycle into coupled carbon, water, energy and nutrient models. Hence, the ingredients for such systems are a carbon cycle model, an algorithm for the assimilation, and systematic and 10 well error-characterized observations relevant to the carbon cycle. Relevant observations for assimilation include various in-situ measurements in the atmosphere (e.g. concentrations of CO2 and other gases) and on land (e.g. fluxes of carbon water and energy, carbon stocks) as well as remote sensing observations (e.g. atmospheric composition, vegetation and surface properties).We briefly review the different existing data assimilation techniques and contrast them to model 15 benchmarking and evaluation efforts (which also rely on observations). A common requirement for all assimilation techniques is a full description of the observational data properties. Uncertainty estimates of the observations are as important as the observations themselves because they similarly determine the outcome of such assimilation systems. Hence, this article reviews the requirements of data assimilation systems on observations and provides a non-exhaustive overview of current 20 observations and their uncertainties for use in terrestrial carbon cycle data assimilation. We report on progress since the review of model-data synthesis in terrestrial carbon observations by Raupach et al. (2005) emphasising the rapid advance in relevant space-based observations.

scholarly journals Impact of Lightning Data Assimilation on Forecasts of a Leeward Slope Precipitation Event in the Western Margin of the Junggar Basin

2021 ◽  
Vol 13 (18) ◽  
pp. 3584
Author(s):  
Peng Liu ◽  
Yi Yang ◽  
Yu Xin ◽  
Chenghai Wang
Keyword(s):  
Water Vapor ◽  
Data Assimilation ◽  
Three Dimensional ◽  
Junggar Basin ◽  
Precipitation Event ◽  
Short Term ◽  
Western Margin ◽  
Precipitation Prediction ◽  
Analysis Experiment ◽  
Short Term Forecasting

A moderate precipitation event occurring in northern Xinjiang, a region with a continental climate with little rainfall, and in leeward slope areas influenced by topography is important but rarely studied. In this study, the performance of lightning data assimilation is evaluated in the short-term forecasting of a moderate precipitation event along the western margin of the Junggar Basin and eastern Jayer Mountain. Pseudo-water vapor observations driven by lightning data are assimilated in both single and cycling analysis experiments of the Weather Research and Forecast (WRF) three-dimensional variational (3DVAR) system. Lightning data assimilation yields a larger increment in the relative humidity in the analysis field at the observed lightning locations, and the largest increment is obtained in the cycling analysis experiment. Due to the increase in water vapor content in the analysis field, more suitable thermal and dynamic conditions for moderate precipitation are obtained on the leeward slope, and the ice-phase and raindrop particle contents increase in the forecast field. Lightning data assimilation significantly improves the short-term leeward slope moderate precipitation prediction along the western margin of the Junggar Basin and provides the best forecast skill in cycling analysis experiments.

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