Disentangling the mechanisms of wave-convection coupling in the tropics

2020 ◽  
Author(s):  
Corinna Hoose ◽  
Hyunju Jung ◽  
Peter Knippertz ◽  
Tijana Janjic ◽  
Yvonne Ruckstuhl ◽  
...  
Keyword(s):  
Data Assimilation ◽  
General Circulation ◽  
Rainfall Variability ◽  
Weather Prediction ◽  
Equatorial Waves ◽  
Model Errors ◽  
Grid Spacing ◽  
Budget Analysis ◽  
The Tropics ◽  
Horizontal Grid

<p><span><span>Tropical weather prediction remains one of the main challenges in atmospheric science due to a combination of insufficient observations, data assimilation algorithms optimized for midlatitudes and large model errors. Due to a strong dependency of many people in the tropics on rainfall variability, combined with a high vulnerability, improved precipitation forecasts have the potential to create substantial benefits in areas such as agriculture, water management, energy production and disease prevention.</span></span></p><p><span><span>Recent studies found that the coupling of equatorial waves to convection is key to improving weather forecasts in the tropics on the synoptic to subseasonal timescale but many models struggle to realistically represent this coupling. Here we use aquaplanet simulations with the ICOsahedral Nonhydrostatic (ICON) model with a 13 km horizontal grid spacing to study the underlying mechanisms of convectively coupled equatorial waves in an idealized framework. We filter the divergence at 200 hPa using a standard wave filtering tool tapering to zero that allows us to identify dynamical characteristics of convectively coupled waves in our simulations. To diagnose thermodynamical aspects of wave-convection couplings, we compare the obtained waves to the total precipitable water and analyze the spatial variance of the budget analysis for column-integrated moist static energy. The same filtering tool and diagnostics are carried out on a realistic ICON simulation with a 2.5 km horizontal grid spacing from the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains (DYAMOND) project.</span></span></p><p><span><span>In the future we plan to run and analyze idealized tropical channel simulations with 2.5 km horizontal resolution, i.e. using the same grid spacing as in the DYAMOND simulation. The comparison between the idealized and the realistic simulations identifies mechanisms of wave-convection coupling. In addition, we will apply this set of diagnostics to forecast experiments using different approaches of data assimilation.</span></span></p><p> </p>

scholarly journals Disentangling the mechanisms of wave-convection coupling using idealized simulations in a tropical channel: wave composites of the moist static energy budget

2021 ◽  
Author(s):  
Hyunju Jung ◽  
Peter Knippertz ◽  
Corinna Hoose ◽  
Yvonne Ruckstuhl ◽  
Robert Redl ◽  
...  
Keyword(s):  
General Circulation ◽  
Equatorial Waves ◽  
Moist Static Energy ◽  
Shallow Convection ◽  
Grid Spacing ◽  
Budget Analysis ◽  
Fourier Filtering ◽  
Static Energy ◽  
Channel Configuration ◽  
Horizontal Grid

<p>Recent studies found that the coupling of equatorial waves to convection is key to improving weather forecasts in the tropics on the synoptic to the subseasonal timescale but many models struggle to realistically represent this coupling. To study the underlying mechanisms of convectively coupled equatorial waves, we use aquaplanet simulations with the ICOsahedral Nonhydrostatic (ICON) model in a tropical channel configuration with a horizontal grid spacing of 13 km and with a prescribed zonally symmetric, latitudinally varying sea surface temperature. We compare simulations with parameterized and explicit deep/shallow convection. Using wave identification tools that are based on Fourier filtering in time and space and on projections of dynamical fields on theoretical wave patterns, we observe a predominance of equator-symmetric equatorial waves such as Kelvin waves and slow large-scale variability resembling the Madden-Julian Oscillation.</p><p>To diagnose interactions between the equatorial waves and convection, we use a moist static energy (MSE) framework. A budget analysis for column integrated MSE shows that spatial anomalies of the net shortwave and longwave radiation and the surface enthalpy flux increase the spatial variance of the column MSE, while advection dampens variability. For wave-convection coupling we employ a wave composite technique for the terms of the MSE budget. Results from this analysis will be presented at the conference. The same filtering tools and diagnostics are applied to a realistic ICON simulation with a 2.5 km horizontal grid spacing from the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains (DYAMOND) project.</p>

scholarly journals Subseasonal Weather Prediction in a Global Convection-Permitting Model

2019 ◽  
Vol 100 (6) ◽  
pp. 1079-1089 ◽  
Author(s):  
Nicholas J. Weber ◽  
Clifford F. Mass
Keyword(s):  
Time Scales ◽  
Numerical Models ◽  
Weather Prediction ◽  
Small Sample ◽  
Tropical Convection ◽  
Grid Spacing ◽  
Tropical Precipitation ◽  
Predictive Skill ◽  
The Tropics ◽  
Horizontal Grid

AbstractAlthough accurate weather and climate prediction beyond one to two weeks is of great value to society, the skill of such extended prediction is limited in current operational global numerical models, whose coarse horizontal grid spacing necessitates the parameterization of atmospheric processes. Of particular concern is the parameterization of convection and specifically convection in the tropics, which impacts global weather at all time scales through atmospheric teleconnections. Convection-permitting models, which forego convective parameterization by explicitly resolving cumulus-scale motions using fine (1–4 km) horizontal grid spacing, can improve global prediction at extended time scales by more faithfully simulating tropical convection and associated teleconnections. This study demonstrates that convection-permitting resolution in a global numerical model can improve both the statistical features of tropical precipitation and extended predictive skill in the tropics and midlatitudes. Comparing four monthlong global simulations with 3-km grid spacing to coarser-resolution simulations that parameterize convection reveals that convection-permitting simulations improve tropical precipitation rates and the diurnal cycle of tropical convection. The propagation of the Madden–Julian oscillation was better predicted in three of the four 3-km simulations; these three runs also featured more skillful prediction of weekly extratropical circulation anomalies, particularly during week 3 of each forecast. These results, though based on a small sample of four cases, demonstrate that convection-permitting global modeling can benefit extended atmospheric prediction and offers the potential for improved operational subseasonal forecast skill.

scholarly journals Cloud and Precipitation Parameterizations in Modeling and Variational Data Assimilation: A Review

2007 ◽  
Vol 64 (11) ◽  
pp. 3766-3784 ◽  
Author(s):  
Philippe Lopez
Keyword(s):  
Data Assimilation ◽  
General Circulation ◽  
Large Scale ◽  
Weather Prediction ◽  
General Circulation Models ◽  
Variational Data Assimilation ◽  
Current Status ◽  
General Question ◽  
Observation Error ◽  
Moist Processes

Abstract This paper first reviews the current status, issues, and limitations of the parameterizations of atmospheric large-scale and convective moist processes that are used in numerical weather prediction and climate general circulation models. Both large-scale (resolved) and convective (subgrid scale) moist processes are dealt with. Then, the general question of the inclusion of diabatic processes in variational data assimilation systems is addressed. The focus is put on linearity and resolution issues, the specification of model and observation error statistics, the formulation of the control vector, and the problems specific to the assimilation of observations directly affected by clouds and precipitation.

scholarly journals Scale-Dependent Representation of the Information Content of Observations in the Global Ensemble Kalman Filter Data Assimilation

2016 ◽  
Vol 144 (8) ◽  
pp. 2927-2945
Author(s):  
Nedjeljka Žagar ◽  
Jeffrey Anderson ◽  
Nancy Collins ◽  
Timothy Hoar ◽  
Kevin Raeder ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Information Content ◽  
Ensemble Kalman Filter ◽  
Large Scale ◽  
Variance Reduction ◽  
Weather Prediction ◽  
Current Data ◽  
Model Framework ◽  
The Tropics

Abstract Global data assimilation systems for numerical weather prediction (NWP) are characterized by significant uncertainties in tropical analysis fields. Furthermore, the largest spread of global ensemble forecasts in the short range on all scales is in the tropics. The presented results suggest that these properties hold even in the perfect-model framework and the ensemble Kalman filter data assimilation with a globally homogeneous network of wind and temperature profiles. The reasons for this are discussed by using the modal analysis, which provides information about the scale dependency of analysis and forecast uncertainties and information about the efficiency of data assimilation to reduce the prior uncertainties in the balanced and inertio-gravity dynamics. The scale-dependent representation of variance reduction of the prior ensemble by the data assimilation shows that the peak efficiency of data assimilation is on the synoptic scales in the midlatitudes that are associated with quasigeostrophic dynamics. In contrast, the variance associated with the inertia–gravity modes is less successfully reduced on all scales. A smaller information content of observations on planetary scales with respect to the synoptic scales is discussed in relation to the large-scale tropical uncertainties that current data assimilation methodologies do not address successfully. In addition, it is shown that a smaller reduction of the large-scale uncertainties in the prior state for NWP in the tropics than in the midlatitudes is influenced by the applied radius for the covariance localization.

scholarly journals The Generation and Forecast of Extreme Winds during the Origin and Progression of the 2017 Tubbs Fire

Atmosphere ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 462 ◽  
Author(s):  
Janice Coen ◽  
Wilfrid Schroeder ◽  
Brad Quayle
Keyword(s):  
High Speed ◽  
Weather Prediction ◽  
Fire Behavior ◽  
Grid Spacing ◽  
Topographic Features ◽  
Wind Event ◽  
Number Flow ◽  
Strong Winds ◽  
Computer Processor ◽  
Horizontal Grid

On 8–9 October 2017, fourteen wildfires developed rapidly during a strong Diablo wind event in northern California including the Tubbs Fire, which travelled over 19 km in 3.25 h. Here, we applied the CAWFE® coupled numerical weather prediction-fire modeling system to investigate the airflow regime and extreme wind peaks underlying the extreme fire behavior using simulations that refine from a 10 km to a 185 m horizontal grid spacing. We found that as Diablo winds travelled south down the Sacramento Valley and fanned out southwestward over the Wine Country, their strength waxed and waned and their direction wavered, creating varying locations near fire origins where wind overrunning topography reached 30–40 m/s, along with streaks and bursts of strong winds in the lee of some topographic features and stagnation downstream of others. Despite a statically stable layer in the lowest 1.5 km, the high Froude number flow sometimes resembled a hydraulic jump. Elsewhere, the flow behaved similarly to neutrally-stratified flow over small hills, creating wind extrema that exceeded 40 m/s at the crest of some lesser hills including near the Tubbs fire ignition, but which shed bursts of high speed winds that travel downstream at approximately 5–7-min intervals. Nonetheless, simulated fire growth lagged satellite detection of fire arrival in Santa Rosa by up to 1 h, although whether the data detect fire line or spotting is ambiguous. A forecast simulation with a 370 m horizontal grid spacing produced an on-time fire line arrival in Santa Rosa, with calculations executed 4 times faster than real time on a single computer processor.

Ensemble Data Assimilation to Characterize Surface-Layer Errors in Numerical Weather Prediction Models

2013 ◽  
Vol 141 (6) ◽  
pp. 1804-1821 ◽  
Author(s):  
J. P. Hacker ◽  
W. M. Angevine
Keyword(s):  
Surface Layer ◽  
Data Assimilation ◽  
Atmospheric Surface Layer ◽  
Weather Prediction ◽  
Model Error ◽  
Time Of Day ◽  
Turbulent Heat ◽  
Model Errors ◽  
Ensemble Data Assimilation ◽  
Ensemble Data

Abstract Experiments with the single-column implementation of the Weather Research and Forecasting Model provide a basis for deducing land–atmosphere coupling errors in the model. Coupling occurs both through heat and moisture fluxes through the land–atmosphere interface and roughness sublayer, and turbulent heat, moisture, and momentum fluxes through the atmospheric surface layer. This work primarily addresses the turbulent fluxes, which are parameterized following the Monin–Obukhov similarity theory applied to the atmospheric surface layer. By combining ensemble data assimilation and parameter estimation, the model error can be characterized. Ensemble data assimilation of 2-m temperature and water vapor mixing ratio, and 10-m wind components, forces the model to follow observations during a month-long simulation for a column over the well-instrumented Atmospheric Radiation Measurement (ARM) Central Facility near Lamont, Oklahoma. One-hour errors in predicted observations are systematically small but nonzero, and the systematic errors measure bias as a function of local time of day. Analysis increments for state elements nearby (15 m AGL) can be too small or have the wrong sign, indicating systematically biased covariances and model error. Experiments using the ensemble filter to objectively estimate a parameter controlling the thermal land–atmosphere coupling show that the parameter adapts to offset the model errors, but that the errors cannot be eliminated. Results suggest either structural errors or further parametric errors that may be difficult to estimate. Experiments omitting atypical observations such as soil and flux measurements lead to qualitatively similar deductions, showing the potential for assimilating common in situ observations as an inexpensive framework for deducing and isolating model errors.

scholarly journals An Evaluation of MÉRA, a High-Resolution Mesoscale Regional Reanalysis

2018 ◽  
Vol 57 (9) ◽  
pp. 2179-2196 ◽  
Author(s):  
Eoin Whelan ◽  
Emily Gleeson ◽  
John Hanley
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Regional Climate ◽  
Prediction System ◽  
High Quality ◽  
Regional Reanalysis ◽  
Horizontal Grid ◽  
Very High

AbstractMet Éireann, the Irish Meteorological Service, has generated a very high resolution (2.5-km horizontal grid) regional climate reanalysis for Ireland called the Met Éireann Reanalysis (MÉRA). MÉRA spans the period from 1981 to 2015 and was produced using the shared ALADIN–HIRLAM numerical weather prediction system. This article includes comparisons with the ERA-Interim and Uncertainties in Ensembles of Regional Reanalyses (UERRA) datasets, analysis of data assimilation outputs, precipitation comparisons, and a focus on extremes of wind and rainfall. The comparisons with the reanalysis datasets show that MÉRA provides a high-quality reconstruction of recent Irish climate and benefits from the use of a very high resolution grid, in particular in relation to wind and precipitation extremes.

scholarly journals Coupled Stratospheric Chemistry-Meteorology Data Assimilation. Part II: Weak and Strong Coupling

2019 ◽  
Author(s):  
Richard Ménard ◽  
Pierre Gauthier ◽  
Yves Rochon ◽  
Alain Robichaud ◽  
Jean de Grandpré ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Strong Coupling ◽  
Zonal Wind ◽  
Surf Zone ◽  
Weather Prediction ◽  
Added Value ◽  
Stratospheric Chemistry ◽  
Temperature Bias ◽  
Global Environmental ◽  
The Tropics

We examine data assimilation coupling between meteorology and chemistry in the stratosphere from both weak and strong coupling strategies. The study was performed with the Canadian operational weather prediction Global Environmental Multiscale (GEM) model coupled online with the photochemical stratospheric chemistry developed at the Belgian Institute for Space Aeronomy, described in Part I. Here, the Canadian Meteorological Centre’s operational variational assimilation system was extended to include errors of chemical variables and cross-covariances between meteorological and chemical variables in a 3D-Var configuration, and we added the adjoint of tracer advection in the 4D-Var configuration. Our results show that the assimilation of limb sounding observations from the MIPAS instrument on board Envisat can be used to anchor the AMSU-A radiance bias correction scheme. Also, the added value of limb sounding temperature observations on meteorology and transport is shown to be significant. Weak coupling data assimilation with ozone-radiation interaction is shown to give comparable on meteorology whether a simplified linearized or comprehensive ozone chemistry scheme is used. Strong coupling data assimilation, using static error cross-covariances between ozone and temperature in a 3D-Var context, produced inconclusive results with the approximations we used. We have also conducted the assimilation of long-lived species observations using 4D-Var to infer winds. Our results showed the added value of assimilating several long-lived species, and an improvement in the zonal wind in the Tropics within the troposphere and lower stratosphere. 4D-Var assimilation also induced a correction of zonal wind in the surf zone and a temperature bias in the lower tropical stratosphere

GEOS-MITgcm coupled atmosphere-ocean simulation for DYAMOND

2021 ◽  
Author(s):  
Ehud Strobach ◽  
Andrea Molod ◽  
Atanas Trayanov ◽  
William Putman ◽  
Dimitris Menemenlis ◽  
...  
Keyword(s):  
General Circulation ◽  
Circulation Model ◽  
Atmospheric Model ◽  
Ocean Model ◽  
Second Phase ◽  
Massachusetts Institute Of Technology ◽  
Grid Spacing ◽  
Institute Of Technology ◽  
Horizontal Grid ◽  
Ocean Simulation

<p>During the past few years, the Goddard Earth Observing System (GEOS) and Massachusetts Institute of Technology general circulation model (MITgcm) groups have produced, respectively, global atmosphere-only and ocean-only simulations with km-scale grid spacing. These simulations have proved invaluable for process studies and the development of satellite and in-situ sampling strategies. Nevertheless, a key limitation of these simulations is the lack of feedback between the ocean and the atmosphere, limiting their usefulness for studying air-sea interactions and designing observing missions to study these interactions. To remove this limitation, we have coupled the km-scale GEOS atmospheric model with the km-scale MITgcm ocean model. We will present preliminary results from the GEOS-MITgcm contribution to the second phase of the DYAMOND (DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains) initiative.</p><p>The coupled atmosphere-ocean simulation was integrated using a cubed-sphere-1440 (~6-7 km horizontal grid spacing) configuration of GEOS and a lat-lon-cap-2160 (2–5-km horizontal grid spacing) configuration of MITgcm. We will show results from a preliminary analysis of air-sea interactions between Sea Surface Temperature (SST) and surface winds. In particular, we will discuss non-local atmospheric overturning circulation formed above the Gulf Stream SST front with characteristic sub-mesoscale width. This formation of a secondary circulation above the front suggests that capturing such air-sea interaction phenomena requires high-resolution capabilities in both the models' oceanic and atmospheric components.</p>

scholarly journals Parameter Sensitivity of the WRF–LETKF System for Assimilation of Radar Observations: Imperfect-Model Observing System Simulation Experiments

2020 ◽  
Vol 35 (4) ◽  
pp. 1345-1362 ◽  
Author(s):  
Paula Maldonado ◽  
Juan Ruiz ◽  
Celeste Saulo
Keyword(s):  
Data Assimilation ◽  
Short Range ◽  
System Simulation ◽  
Weather Prediction ◽  
Doppler Velocity ◽  
Convective System ◽  
Model Errors ◽  
Spatiotemporal Resolution ◽  
Simulation Experiments ◽  
The Impact

AbstractSpecification of suitable initial conditions to accurately forecast high-impact weather events associated with intense thunderstorms still poses a significant challenge for convective-scale forecasting. Radar data assimilation has been showing encouraging results to produce an accurate estimate of the state of the atmosphere at the mesoscale, as it combines high-spatiotemporal-resolution observations with convection-permitting numerical weather prediction models. However, many open questions remain regarding the configuration of state-of-the-art data assimilation systems at the mesoscale and their potential impact upon short-range weather forecasts. In this work, several observing system simulation experiments of a mesoscale convective system were performed to assess the sensitivity of the local ensemble transform Kalman filter to both relaxation-to-prior spread (RTPS) inflation and horizontal localization of the error covariance matrix. Realistic large-scale forcing and model errors have been taken into account in the simulation of reflectivity and Doppler velocity observations. Overall, the most accurate analyses in terms of RMSE were produced with a relatively small horizontal localization cutoff radius (~3.6–7.3 km) and large RTPS inflation parameter (~0.9–0.95). Additionally, the impact of horizontal localization on short-range ensemble forecast was larger compared to inflation, almost doubling the lead times up to which the effect of using a more accurate state to initialize the forecast persisted.

Sign in / Sign up

Export Citation Format

Share Document