scholarly journals Effect of boundary conditions on adjoint-based forecast sensitivity observation impact in a regional model

2021 ◽  
Author(s):  
Hyun Mee Kim ◽  
Dae-Hui Kim
Keyword(s):  
Boundary Conditions ◽  
Forecast Error ◽  
Error Reduction ◽  
Regional Model ◽  
Buffer Size ◽  
Lateral Boundary ◽  
Modeling Framework ◽  
Lateral Boundary Conditions ◽  
Observation Impact ◽  
The Impact

AbstractIn this study, the effect of boundary condition configurations in the regional Weather Research and Forecasting (WRF) model on the adjoint-based forecast sensitivity observation impact (FSOI) for 24 h forecast error reduction was evaluated. The FSOI has been used to diagnose the impact of observations on the forecast performance in several global and regional models. Different from the global model, in the regional model, the lateral boundaries affect forecasts and FSOI results. Several experiments with different lateral boundary conditions were conducted. The experimental period was from 1 to 14 June 2015. With or without data assimilation, the larger the buffer size in lateral boundary conditions, the smaller the forecast error. The nonlinear and linear forecast error reduction (i.e., observation impact) decreased as the buffer size increased, implying larger impact of lateral boundaries and smaller observation impact on the forecast error. In all experiments, in terms of observation types (variables), upper-air radiosonde observations (brightness temperature) exhibited the greatest observation impact. The ranking of observation impacts was consistent for observation types and variables among experiments with a constraint in the response function at the upper boundary. The fractions of beneficial observations were approximately 60%, and did not considerably vary depending on the boundary conditions specified when calculating the FSOI in the regional modeling framework.

scholarly journals Estimating the Uncertainty in a Regional Climate Model Related to Initial and Lateral Boundary Conditions

2005 ◽  
Vol 18 (7) ◽  
pp. 917-933 ◽  
Author(s):  
Wanli Wu ◽  
Amanda H. Lynch ◽  
Aaron Rivers
Keyword(s):  
Boundary Conditions ◽  
Climate Model ◽  
Initial Conditions ◽  
Climate Modeling ◽  
Regional Climate ◽  
Regional Climate Modeling ◽  
Regional Model ◽  
Lateral Boundary ◽  
Lateral Boundary Conditions ◽  
The Impact

Abstract There is a growing demand for regional-scale climate predictions and assessments. Quantifying the impacts of uncertainty in initial conditions and lateral boundary forcing data on regional model simulations can potentially add value to the usefulness of regional climate modeling. Results from a regional model depend on the realism of the driving data from either global model outputs or global analyses; therefore, any biases in the driving data will be carried through to the regional model. This study used four popular global analyses and achieved 16 driving datasets by using different interpolation procedures. The spread of the 16 datasets represents a possible range of driving data based on analyses to the regional model. This spread is smaller than typically associated with global climate model realizations of the Arctic climate. Three groups of 16 realizations were conducted using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) in an Arctic domain, varying both initial and lateral boundary conditions, varying lateral boundary forcing only, and varying initial conditions only. The response of monthly mean atmospheric states to the variations in initial and lateral driving data was investigated. Uncertainty in the regional model is induced by the interaction between biases from different sources. Because of the nonlinearity of the problem, contributions from initial and lateral boundary conditions are not additive. For monthly mean atmospheric states, biases in lateral boundary conditions generally contribute more to the overall uncertainty than biases in the initial conditions. The impact of initial condition variations decreases with the simulation length while the impact of variations in lateral boundary forcing shows no clear trend. This suggests that the representativeness of the lateral boundary forcing plays a critical role in long-term regional climate modeling. The extent of impact of the driving data uncertainties on regional climate modeling is variable dependent. For some sensitive variables (e.g., precipitation, boundary layer height), even the interior of the model may be significantly affected.

scholarly journals Comparing Impacts of Satellite Data Assimilation and Lateral Boundary Conditions on Regional Model Forecasting: A Case Study of Hurricane Sandy (2012)

2017 ◽  
Vol 32 (2) ◽  
pp. 595-608
Author(s):  
Tong Zhu ◽  
Sid Ahmed Boukabara ◽  
Kevin Garrett
Keyword(s):  
Data Assimilation ◽  
Boundary Conditions ◽  
Satellite Data ◽  
Regional Model ◽  
Hurricane Sandy ◽  
Track Forecast ◽  
Lateral Boundary ◽  
Lateral Boundary Conditions ◽  
Outer Domain ◽  
The Impact

Abstract The impacts of both satellite data assimilation (DA) and lateral boundary conditions (LBCs) on the Hurricane Weather Research and Forecasting (HWRF) Model forecasts of Hurricane Sandy 2012 were assessed. To investigate the impact of satellite DA, experiments were run with and without satellite data assimilated, as well as with all satellite data but excluding Geostationary Operational Environmental Satellite (GOES) Sounder data. To gauge the LBC impact, these experiments were also run with a variety of outer domain (D-1) sizes. The inclusion of satellite DA resulted in analysis fields that better characterized the tropical storm structures including the warm core anomaly and wavenumber-1 asymmetry near the eyewall, and also served to reduce the forecast track errors for Hurricane Sandy. The specific impact of assimilating the GOES Sounder data showed positive impacts on forecasts of the storm minimum sea level pressure. Increasing the D-1 size resulted in increases in the day 4/5 forecast track errors when verified against the best track and the Global Forecast System (GFS) forecast, which dominated any benefits from assimilating an increased volume of satellite observations due to the larger domain. It was found that the LBCs with realistic environmental flow information could provide better constraints on smaller domain forecasts. This study demonstrated that satellite DA can improve the analysis of a hurricane asymmetry, especially in a shear environment, and then lead to a better track forecast, and also emphasized the importance of the LBCs and the challenges associated with the evaluation of satellite data impacts on regional model prediction.

scholarly journals Cloud-Resolving Ensemble Simulations of Mediterranean Heavy Precipitating Events: Uncertainty on Initial Conditions and Lateral Boundary Conditions

2011 ◽  
Vol 139 (2) ◽  
pp. 403-423 ◽  
Author(s):  
Benoît Vié ◽  
Olivier Nuissier ◽  
Véronique Ducrocq
Keyword(s):  
Boundary Conditions ◽  
Case Studies ◽  
Short Range ◽  
Initial Conditions ◽  
Ensemble Prediction ◽  
Lateral Boundary ◽  
Synoptic Scale ◽  
Lateral Boundary Conditions ◽  
Convective Scale ◽  
The Impact

Abstract This study assesses the impact of uncertainty on convective-scale initial conditions (ICs) and the uncertainty on lateral boundary conditions (LBCs) in cloud-resolving simulations with the Application of Research to Operations at Mesoscale (AROME) model. Special attention is paid to Mediterranean heavy precipitating events (HPEs). The goal is achieved by comparing high-resolution ensembles generated by different methods. First, an ensemble data assimilation technique has been used for assimilation of perturbed observations to generate different convective-scale ICs. Second, three ensembles used LBCs prescribed by the members of a global short-range ensemble prediction system (EPS). All ensembles obtained were then evaluated over 31- and/or 18-day periods, and on 2 specific case studies of HPEs. The ensembles are underdispersive, but both the probabilistic evaluation of their overall performance and the two case studies confirm that they can provide useful probabilistic information for the HPEs considered. The uncertainty on convective-scale ICs is shown to have an impact at short range (under 12 h), and it is strongly dependent on the synoptic-scale context. Specifically, given a marked circulation near the area of interest, the imposed LBCs rapidly overwhelm the initial differences, greatly reducing the spread of the ensemble. The uncertainty on LBCs shows an impact at longer range, as the spread in the coupling global ensemble increases, but it also depends on the synoptic-scale conditions and their predictability.

The impact of chemical lateral boundary conditions on CMAQ predictions of tropospheric ozone over the continental United States

2008 ◽  
Vol 9 (1) ◽  
pp. 43-58 ◽  
Author(s):  
Youhua Tang ◽  
Pius Lee ◽  
Marina Tsidulko ◽  
Ho-Chun Huang ◽  
Jeffery T. McQueen ◽  
...  
Keyword(s):  
United States ◽  
Boundary Conditions ◽  
Tropospheric Ozone ◽  
Lateral Boundary ◽  
Lateral Boundary Conditions ◽  
The Impact

scholarly journals Soil Field Model Interoperability: Challenges and Impact on Screen Temperature Forecast Skill during the Nordic Winter

2012 ◽  
Vol 13 (4) ◽  
pp. 1215-1232 ◽  
Author(s):  
Jørn Kristiansen ◽  
Dag Bjørge ◽  
John M. Edwards ◽  
Gabriel G. Rooney
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Boundary Conditions ◽  
Limited Area ◽  
Lateral Boundary ◽  
Error Source ◽  
Main Error ◽  
Lateral Boundary Conditions ◽  
The Impact ◽  
Grid Length

Abstract The high-resolution (4-km grid length) Met Office (UKMO) Unified Model forecasts driven by the coarser-resolution (8-km grid length) High-Resolution Limited-Area Model (HIRLAM), UM4, often produce significantly colder screen-level (2 m) temperatures in winter over Norway than forecast with HIRLAM itself. To diagnose the main error source of this cold bias this study focuses on the forecast initial and lateral boundary conditions, particularly the initialization of soil moisture and temperature. The soil variables may be used differently by land surface schemes of varying complexity, representing a challenge to model interoperability. In a set of five experiments, daily UM4 forecasts are driven by alternating initial and lateral boundary conditions from two different parent models: HIRLAM and Met Office North Atlantic and Europe (NAE). The experiment period is November 2007. Points for scientific examination into the topics of model interoperability and sensitivity to soil initial conditions are identified. The soil moisture is the main error source and is therefore important also in winter, rather than being a challenge only in summer. The day-to-day variability in the forecast error is large with the larger errors on days with strong longwave heat loss at the surface (i.e., the forecast sensitivity to soil moisture content is significant but variable). The much drier soil in HIRLAM compared to NAE reduces the heat capacity of the soil layers and affects the heat flux from the surface soil layer to the surface. Normalizing the respective soil moisture fields reduces these differences. The impact of ground snow is quite limited.

Impacts of Mountain–Plains Solenoid on Diurnal Variations of Rainfalls along the Mei-Yu Front over the East China Plains

2012 ◽  
Vol 140 (2) ◽  
pp. 379-397 ◽  
Author(s):  
Jianhua Sun ◽  
Fuqing Zhang
Keyword(s):  
Boundary Conditions ◽  
Diurnal Variations ◽  
Central China ◽  
East China ◽  
Lateral Boundary ◽  
Moist Convection ◽  
Lateral Boundary Conditions ◽  
Mesoscale Convective ◽  
The Impact ◽  
Convective Vortices

Convection-permitting numerical experiments using the Weather Research and Forecasting (WRF) model are performed to examine the impact of a thermally driven mountain–plains solenoid (MPS) on the diurnal variations of precipitation and mesoscale convective vortices along the mei-yu front over the east China plains during 1–10 July 2007. The focus of the analyses is a 10-day simulation that used the 10-day average of the global analysis at 0000 UTC as the initial condition and the 10-day averages every 6 h as lateral boundary conditions (with diurnal variations only). Despite differences in the rainfall intensity and location, this idealized experiment successfully simulated the observed diurnal variation and eastward propagation of rainfall and mesoscale convective vortices along the mei-yu front. It was found that the upward branch of the MPS, along with the attendant nocturnal low-level jet, is primarily responsible for the midnight-to-early-morning rainfall enhancement along the mei-yu front. The MPS is induced by differential heating between the high mountain ranges in central China and the low-lying plains in east China. Diabatic heating from moist convection initiated and/or enhanced by the solenoid circulation subsequently leads to the formation of a mesoscale convective vortex that further organizes and amplifies moist convection while propagating eastward along the mei-yu front. The downward branch of the MPS, on the other hand, leads to the suppression of precipitation over the plains during the daytime. The impacts of this regional MPS on the rainfall diurnal variations are further attested to by another idealized WRF simulation that uses fixed lateral boundary conditions.

scholarly journals Uncertainty of lateral boundary conditions in a convection-permitting ensemble: a strategy of selection for Mediterranean heavy precipitation events

2012 ◽  
Vol 12 (10) ◽  
pp. 2993-3011 ◽  
Author(s):  
O. Nuissier ◽  
B. Joly ◽  
B. Vié ◽  
V. Ducrocq
Keyword(s):  
Cluster Analysis ◽  
Boundary Conditions ◽  
Heavy Precipitation ◽  
Lateral Boundary ◽  
Low Level ◽  
Ensemble Simulations ◽  
Lateral Boundary Conditions ◽  
The Impact ◽  
Precipitation Events ◽  
Selection Of

Abstract. This study examines the impact of lateral boundary conditions (LBCs) in convection-permitting (C-P) ensemble simulations with the AROME model driven by the ARPEGE EPS (PEARP). Particular attention is paid to two torrential rainfall episodes, observed on 15–16 June 2010 (the Var case) and 7–8 September 2010 (the Gard-Ardèche case) over the southeastern part of France. Regarding the substantial computing time for convection-permitting models, a methodology of selection of a few LBCs, dedicated for C-P ensemble simulations of heavy precipitation events is evaluated. Several sensitivity experiments are carried out to evaluate the skill of the AROME ensembles, using different approaches for selection of the driving PEARP members. The convective-scale predictability of the Var case is very low and it is driven primarily by a surface low over the Gulf of Lyon inducing a strong convergent low-level flow, and accordingly advecting strong moisture supply from the Mediterranean Sea toward the flooded area. The Gard-Ardèche case is better handled in ensemble simulations as a surface cold front moved slowly eastwards while increasing the low-level water vapour ahead is well reproduced. The selection based on a cluster analysis of the PEARP members generally better performs against a random selection. The consideration of relevant meteorological parameters for the convective events of interest (i.e. geopotential height at 500 hPa and horizontal moisture flux at 925 hPa) refined the cluster analysis. It also helps in better capturing the forecast uncertainty variability which is spatially more localized at the "high-impact region" due to the selection of more mesoscale parameters.

Impact of non-Arctic observations on the AROME-Arctic regional model

2020 ◽  
Author(s):  
Rogerr Randriamampianina
Keyword(s):  
Regional Model ◽  
Satellite Observations ◽  
Lateral Boundary ◽  
Regional Data ◽  
Weather Forecasts ◽  
Lateral Boundary Conditions ◽  
Skill Scores ◽  
Medium Range ◽  
Considerable Impact ◽  
The Impact

<p>In the framework of the Applicate project (https://applicate.eu), ECMWF (European Centre for Medium-Range Weather Forecasts) performed global (Bormann et al. 2019) and Arctic (Lawrence et al. 2019) observing system experiments. Use of the results of these experiments as lateral boundary conditions (LBC) for our regional model opens opportunity to study the following: 1) the impact of observations through regional data assimilation (DA); 2) the impact of observations that are assimilated in a global model through LBC in a regional model; 3) the impact of global loss of observations in a regional model; and 4) the impact of non-Arctic observations in an Arctic regional model.</p><p>In the framework of the Alertness project, we performed experiments for the two special observation periods (SOP) 1 and 2 and found considerable impact (significant for some cases) of both conventional and satellite observations through both regional DA and LBC. So far, the impact of non-Arctic observations on our Arctic regional model AROME-Arctic analyses and forecasts was checked during SOP1 with microwave radiance only. The impact was found to be positive, especially on day-2 forecasts.</p><p>In this presentation, the impact of other non-Arctic observations (conventional and satellite) on our regional model AROME-Arctic will be discussed through different forecast skill scores verification.</p>

scholarly journals Regional Model Nesting within GFS Daily Forecasts Over West Africa

2010 ◽  
Vol 4 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Leonard M. Druyan ◽  
Matthew Fulakeza ◽  
Patrick Lonergan ◽  
Ruben Worrell
Keyword(s):  
West Africa ◽  
Boundary Conditions ◽  
Initial Conditions ◽  
Regional Model ◽  
Precipitation Forecast ◽  
Lateral Boundary ◽  
Systems Research ◽  
Lateral Boundary Conditions ◽  
Realistic Information ◽  
Mean Square Errors

The study uses the RM3, the regional climate model at the Center for Climate Systems Research of Columbia University and the NASA/Goddard Institute for Space Studies (CCSR/GISS). The paper evaluates 30 48-hour RM3 weather forecasts over West Africa during September 2006 made on a 0.5° grid nested within 1° Global Forecast System (GFS) global forecasts. September 2006 was the Special Observing Period # 3 of the African Monsoon Multidisciplinary Analysis (AMMA). Archived GFS initial conditions and lateral boundary conditions for the simulations from the US National Weather Service, National Oceanographic and Atmospheric Administration were interpolated four times daily. Results for precipitation forecasts are validated against Tropical Rainfall Measurement Mission (TRMM) satellite estimates and data from the Famine Early Warning System (FEWS), which includes rain gauge measurements, and forecasts of circulation are compared to reanalysis 2. Performance statistics for the precipitation forecasts include bias, root-mean-square errors and spatial correlation coefficients. The nested regional model forecasts are compared to GFS forecasts to gauge whether nesting provides additional realistic information. They are also compared to RM3 simulations driven by reanalysis 2, representing “high potential skill” forecasts, to gauge the sensitivity of results to lateral boundary conditions. Nested RM3/GFS forecasts generate excessive moisture advection toward West Africa, which in turn causes prodigious amounts of model precipitation. This problem is corrected by empirical adjustments in the preparation of lateral boundary conditions and initial conditions. The resulting modified simulations improve on the GFS precipitation forecasts, achieving time-space correlations with TRMM of 0.77 on the first day and 0.63 on the second day. One realtime RM3/GFS precipitation forecast made at and posted by the African Centre of Meteorological Application for Development (ACMAD) in Niamey, Niger is shown.

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