Understanding the Impact of Radar and In Situ Observations on the Prediction of a Nocturnal Convection Initiation Event on 25 June 2013 Using an Ensemble-Based Multiscale Data Assimilation System

The initiation of new convection at night in the Great Plains contributes to a nocturnal maximum in precipitation and produces localized heavy rainfall and severe weather hazards in the region. Although previous work has evaluated numerical model forecasts and data assimilation (DA) impacts for convection initiation (CI), most previous studies focused only on convection that initiates during the afternoon and not explicitly on nocturnal thunderstorms. In this study, we investigate the impact of assimilating in situ and radar observations for a nocturnal CI event on 25 June 2013 using an ensemble-based DA and forecast system. Results in this study show that a successful CI forecast resulted only when assimilating conventional in situ observations on the inner, convection-allowing domain. Assimilating in situ observations strengthened preexisting convection in southwestern Kansas by enhancing buoyancy and locally strengthening low-level convergence. The enhanced convection produced a cold pool that, together with increased convergence along the northwestern low-level jet (LLJ) terminus near the region of CI, was an important mechanism for lifting parcels to their level of free convection. Gravity waves were also produced atop the cold pool that provided further elevated ascent. Assimilating radar observations further improved the forecast by suppressing spurious convection and reducing the number of ensemble members that produced CI along a spurious outflow boundary. The fact that the successful CI forecasts resulted only when the in situ observations were assimilated suggests that accurately capturing the preconvective environment and specific mesoscale features is especially important for nocturnal CI forecasts.

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The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual timescales

Hydrology and Earth System Sciences ◽

10.5194/hess-19-4831-2015 ◽

2015 ◽

Vol 19 (12) ◽

pp. 4831-4844 ◽

Cited By ~ 19

Author(s):

C. Draper ◽

R. Reichle

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Surface Soil ◽

Root Zone ◽

Surface Model ◽

Surface Soil Moisture ◽

Near Surface ◽

In Situ Observations ◽

The Impact

Abstract. A 9 year record of Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) soil moisture retrievals are assimilated into the Catchment land surface model at four locations in the US. The assimilation is evaluated using the unbiased mean square error (ubMSE) relative to watershed-scale in situ observations, with the ubMSE separated into contributions from the subseasonal (SMshort), mean seasonal (SMseas), and inter-annual (SMlong) soil moisture dynamics. For near-surface soil moisture, the average ubMSE for Catchment without assimilation was (1.8 × 10−3 m3 m−3)2, of which 19 % was in SMlong, 26 % in SMseas, and 55 % in SMshort. The AMSR-E assimilation significantly reduced the total ubMSE at every site, with an average reduction of 33 %. Of this ubMSE reduction, 37 % occurred in SMlong, 24 % in SMseas, and 38 % in SMshort. For root-zone soil moisture, in situ observations were available at one site only, and the near-surface and root-zone results were very similar at this site. These results suggest that, in addition to the well-reported improvements in SMshort, assimilating a sufficiently long soil moisture data record can also improve the model representation of important long-term events, such as droughts. The improved agreement between the modeled and in situ SMseas is harder to interpret, given that mean seasonal cycle errors are systematic, and systematic errors are not typically targeted by (bias-blind) data assimilation. Finally, the use of 1-year subsets of the AMSR-E and Catchment soil moisture for estimating the observation-bias correction (rescaling) parameters is investigated. It is concluded that when only 1 year of data are available, the associated uncertainty in the rescaling parameters should not greatly reduce the average benefit gained from data assimilation, although locally and in extreme years there is a risk of increased errors.

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How essential are Argo observations to constrain a global ocean data assimilation system?

Ocean Science Discussions ◽

10.5194/osd-12-1145-2015 ◽

2015 ◽

Vol 12 (3) ◽

pp. 1145-1186 ◽

Cited By ~ 2

Author(s):

V. Turpin ◽

E. Remy ◽

P. Y. Le Traon

Keyword(s):

Data Assimilation ◽

Forecast Error ◽

Global Ocean ◽

Altimeter Data ◽

Western Boundary ◽

Argo Data ◽

Ocean Data Assimilation ◽

In Situ Observations ◽

The Impact

Abstract. Observing System Experiments (OSEs) are carried out over a one-year period to quantify the impact of Argo observations on the Mercator-Ocean 1/4° global ocean analysis and forecasting system. The reference simulation assimilates sea surface temperature (SST), SSALTO/DUACS altimeter data and Argo and other in situ observations from the Coriolis data center. Two other simulations are carried out where all Argo and half of Argo data sets are withheld. Assimilating Argo observations has a significant impact on analyzed and forecast temperature and salinity fields at different depths. Without Argo data assimilation, large errors occur in analyzed fields as estimated from the differences when compared with in situ observations. For example, in the 0–300 m layer RMS differences between analyzed fields and observations reach 0.25 psu and 1.25 °C in the western boundary currents and 0.1 psu and 0.75 °C in the open ocean. The impact of the Argo data in reducing observation-model forecast error is also significant from the surface down to a depth of 2000 m. Differences between independent observations and forecast fields are thus reduced by 20 % in the upper layers and by up to 40 % at a depth of 2000 m when Argo data are assimilated. At depth, the most impacted regions in the global ocean are the Mediterranean outflow and the Labrador Sea. A significant degradation can be observed when only half of the data are assimilated. All Argo observations thus matter, even with a 1/4° model resolution. The main conclusion is that the performance of global data assimilation systems is heavily dependent on the availability of Argo data.

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The Impact of Spatial Variations of Low-Level Stability on the Life Cycle of a Simulated Supercell Storm

Monthly Weather Review ◽

10.1175/2009mwr3010.1 ◽

2010 ◽

Vol 138 (5) ◽

pp. 1738-1766 ◽

Cited By ~ 47

Author(s):

Conrad L. Ziegler ◽

Edward R. Mansell ◽

Jerry M. Straka ◽

Donald R. MacGorman ◽

Donald W. Burgess

Keyword(s):

Boundary Layer ◽

Life Cycle ◽

Cold Pool ◽

Ambient Flow ◽

Low Level ◽

Inversion Region ◽

Supercell Storm ◽

Outflow Boundary ◽

The Right ◽

The Impact

Abstract This study reports on the dynamical evolution of simulated, long-lived right-moving supercell storms in a high-CAPE, strongly sheared mesoscale environment, which initiate in a weakly capped region and subsequently move into a cold boundary layer (BL) and inversion region before dissipating. The storm simulations realistically approximate the main morphological features and evolution of the 22 May 1981 Binger, Oklahoma, supercell storm by employing time-varying inflow lateral boundary conditions for the storm-relative moving grid, which in turn are prescribed from a parent, fixed steady-state mesoscale analysis to approximate the observed inversion region to the east of the dryline on that day. A series of full life cycle storm simulations have been performed in which the magnitude of boundary layer coldness and the convective inhibition are varied to examine the ability of the storm to regenerate and sustain its main updraft as it moves into environments with increasing convective stability. The analysis of the simulations employs an empirical expression for the theoretical speed of the right-forward-flank outflow boundary relative to the ambient, low-level storm inflow that is consistent with simulated cold-pool boundary movement. The theoretical outflow boundary speed in the direction opposite to the ambient flow increases with an increasing cold-pool temperature deficit relative to the ambient BL temperature, and it decreases as ambient wind speed increases. The right-moving, classic (CL) phase of the simulated supercells is supported by increasing precipitation content and a stronger cold pool, which increases the right-moving cold-pool boundary speed against the constant ambient BL winds. The subsequent decrease of the ambient BL temperature with eastward storm movement decreases the cold-pool temperature deficit and reduces the outflow boundary speed against the ambient winds, progressing through a state of stagnation to an ultimate retrogression of the outflow boundary in the direction of the ambient flow. Onset of a transient, left-moving low-precipitation (LP) phase is initiated as the storm redevelops on the retrograding outflow boundary. The left-moving LP storm induces compensating downward motions in the inversion layer that desiccates the inflow, elevates the cloudy updraft parcel level of free convection (LFC), and leads to the final storm decay. The results demonstrate that inversion-region simulations support isolated, long-lived supercells. Both the degree of stratification and the coldness of the ambient BL regulate the cold-pool intensity and the strength and capacity of the outflow boundary to lift BL air through the LFC and thus regenerate convection, resulting in variation of supercell duration in the inversion region of approximately 1–2 h. In contrast, horizontally homogeneous conditions lacking an inversion region result in the development of secondary convection from the initial isolated supercell, followed by rapid upscale growth after 3 h to form a long-lived mesoscale convective system.

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Radar Data Assimilation with WRF 4D-Var. Part II: Comparison with 3D-Var for a Squall Line over the U.S. Great Plains

Monthly Weather Review ◽

10.1175/mwr-d-12-00169.1 ◽

2013 ◽

Vol 141 (7) ◽

pp. 2245-2264 ◽

Cited By ~ 57

Author(s):

Juanzhen Sun ◽

Hongli Wang

Keyword(s):

Data Assimilation ◽

Radial Velocity ◽

Great Plains ◽

Radar Data ◽

Variational Data Assimilation ◽

Precipitation Forecast ◽

Squall Line ◽

Cold Pool ◽

Low Level ◽

Radar Data Assimilation

Abstract The Weather Research and Forecasting Model (WRF) four-dimensional variational data assimilation (4D-Var) system described in Part I of this study is compared with its corresponding three-dimensional variational data assimilation (3D-Var) system using a Great Plains squall line observed during the International H2O Project. Two 3D-Var schemes are used in the comparison: a standard 3D-Var radar data assimilation (DA) that is the same as the 4D-Var except for the exclusion of the constraining dynamical model and an enhanced 3D-Var that includes a scheme to assimilate an estimated in-cloud humidity field. The comparison is made by verifying their skills in 0–6-h quantitative precipitation forecast (QPF) against stage-IV analysis, as well as in wind forecasts against radial velocity observations. The relative impacts of assimilating radial velocity and reflectivity on QPF are also compared between the 4D-Var and 3D-Var by conducting data-denial experiments. The results indicate that 4D-Var substantially improves the QPF skill over the standard 3D-Var for the entire 6-h forecast range and over the enhanced 3D-Var for most forecast hours. Radial velocity has a larger impact relative to reflectivity in 4D-Var than in 3D-Var in the first 3 h because of a quicker precipitation spinup. The analyses and forecasts from the 4D-Var and 3D-Var schemes are further compared by examining the meridional wind, horizontal convergence, low-level cold pool, and midlevel temperature perturbation, using analyses from the Variational Doppler Radar Analysis System (VDRAS) as references. The diagnoses of these fields suggest that the 4D-Var analyzes the low-level cold pool, its leading edge convergence, and midlevel latent heating in closer resemblance to the VDRAS analyses than the 3D-Var schemes.

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How essential are Argo observations to constrain a global ocean data assimilation system?

Ocean Science ◽

10.5194/os-12-257-2016 ◽

2016 ◽

Vol 12 (1) ◽

pp. 257-274 ◽

Cited By ~ 12

Author(s):

V. Turpin ◽

E. Remy ◽

P. Y. Le Traon

Keyword(s):

Data Assimilation ◽

Surface Temperature ◽

Sea Surface ◽

Global Ocean ◽

Argo Data ◽

In Situ Observations ◽

The Impact ◽

Data Assimilation System ◽

Assimilation System

Abstract. Observing system experiments (OSEs) are carried out over a 1-year period to quantify the impact of Argo observations on the Mercator Ocean 0.25° global ocean analysis and forecasting system. The reference simulation assimilates sea surface temperature (SST), SSALTO/DUACS (Segment Sol multi-missions dALTimetrie, d'orbitographie et de localisation précise/Data unification and Altimeter combination system) altimeter data and Argo and other in situ observations from the Coriolis data center. Two other simulations are carried out where all Argo and half of the Argo data are withheld. Assimilating Argo observations has a significant impact on analyzed and forecast temperature and salinity fields at different depths. Without Argo data assimilation, large errors occur in analyzed fields as estimated from the differences when compared with in situ observations. For example, in the 0–300 m layer RMS (root mean square) differences between analyzed fields and observations reach 0.25 psu and 1.25 °C in the western boundary currents and 0.1 psu and 0.75 °C in the open ocean. The impact of the Argo data in reducing observation–model forecast differences is also significant from the surface down to a depth of 2000 m. Differences between in situ observations and forecast fields are thus reduced by 20 % in the upper layers and by up to 40 % at a depth of 2000 m when Argo data are assimilated. At depth, the most impacted regions in the global ocean are the Mediterranean outflow, the Gulf Stream region and the Labrador Sea. A significant degradation can be observed when only half of the data are assimilated. Therefore, Argo observations matter to constrain the model solution, even for an eddy-permitting model configuration. The impact of the Argo floats' data assimilation on other model variables is briefly assessed: the improvement of the fit to Argo profiles do not lead globally to unphysical corrections on the sea surface temperature and sea surface height. The main conclusion is that the performance of the Mercator Ocean 0.25° global data assimilation system is heavily dependent on the availability of Argo data.

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Improving Afternoon Thunderstorm Prediction over Taiwan through 3DVar-Based Radar and Surface Data Assimilation

Weather and Forecasting ◽

10.1175/waf-d-20-0037.1 ◽

2020 ◽

Vol 35 (6) ◽

pp. 2603-2620

Author(s):

I-Han Chen ◽

Jing-Shan Hong ◽

Ya-Ting Tsai ◽

Chin-Tzu Fong

Keyword(s):

Data Assimilation ◽

Lead Time ◽

Surface Wind ◽

Cold Pool ◽

Model Errors ◽

Radar Observations ◽

Surface Data ◽

Forecast Lead Time ◽

Outflow Boundary ◽

Surface Observations

AbstractRecently, the Central Weather Bureau of Taiwan developed a WRF- and WRF data assimilation (WRFDA)-based convective-scale data assimilation system to increase model predictability toward high-impact weather. In this study, we focus on afternoon thunderstorm (AT) prediction and investigate the following questions: 1) Is the designation of a rapid update cycle strategy with a blending scheme effective? 2) Can surface data assimilation contribute positively to AT prediction under the complex geography of Taiwan island? 3) What is the relative importance between radar and surface observation to AT prediction? 4) Can we increase the AT forecast lead time in the morning through data assimilation? Consecutive ATs from 30 June to 8 July 2017 are investigated. Five experiments, each having 240 continuous cycles, are designed. Results show that employing continuous cycles with a blending scheme mitigates model spinup compared with downscaled forecasts. Although there are few radar echoes before AT initiation, assimilating radar observations is still crucial since it largely corrects model errors in cycles. However, assimilating surface observations is more important compared with radar in terms of extending forecast lead time in the morning. Either radar or surface observations contribute positively, and assimilating both has the highest QPF score. Assimilating surface observations systematically improves surface wind and temperature predictions based on 240 cases. A case study demonstrates that the model can capture the AT initiation and development by assimilating surface and radar observations. Its cold pool and outflow boundary prediction are also improved. In this case, the assimilation of surface wind and water vapor in the morning contributes more compared with temperature and pressure.

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A comparison between polynya area and associated ice production with mooring-based measurements of temperature, salinity and currents in the southwestern Ross Sea, Antarctica

Annals of Glaciology ◽

10.3189/172756411795931705 ◽

2011 ◽

Vol 52 (57) ◽

pp. 291-300 ◽

Cited By ~ 3

Author(s):

Stefan Kern ◽

Stefano Aliani

Keyword(s):

Ross Sea ◽

Time Lag ◽

Ice Formation ◽

Terra Nova Bay ◽

In Situ Observations ◽

Satellite Microwave ◽

Terra Nova ◽

Ice Production ◽

The Impact

AbstractWintertime (April–September) area estimates of the Terra Nova Bay polynya (TNBP), Antarctica, based on satellite microwave radiometry are compared with in situ observations of water salinity, temperature and currents at a mooring in Terra Nova Bay in 1996 and 1997. In 1996, polynya area anomalies and associated anomalies in polynya ice production are significantly correlated with salinity anomalies at the mooring. Salinity anomalies lag area and/or ice production anomalies by about 3 days. Up to 50% of the variability in the salinity at the mooring position can be explained by area and/or ice production anomalies in the TNBP for April–September 1996. This value increases to about 70% when considering shorter periods like April–June or May–July, but reduces to 30% later, for example July–September, together with a slight increase in time lag. In 1997, correlations are smaller, less significant and occur at a different time lag. Analysis of ocean currents at the mooring suggests that in 1996 conditions were more favourable than in 1997 for observing the impact of descending plumes of salt-enriched water formed in the polynya during ice formation on the water masses at the mooring depth.

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Quasi-idealized numerical simulations of processes involved in orogenic convection initiation over the Sierras de Córdoba mountains

Journal of the Atmospheric Sciences ◽

10.1175/jas-d-21-0007.1 ◽

2021 ◽

Author(s):

Christopher A. Davis

Keyword(s):

Numerical Simulations ◽

Mountain Range ◽

Moist Convection ◽

Low Level ◽

Physical Mechanisms ◽

Convergence Line ◽

Synoptic Conditions ◽

Deep Moist Convection ◽

Outflow Boundary ◽

Convection Initiation

Abstract The Sierras de Córdoba (SDC) mountain range in Argentina is a hotspot of deep moist convection initiation (CI). Radar climatology indicates that 44% of daytime CI events that occur near the SDC in spring and summer seasons and that are not associated with the passage of a cold front or an outflow boundary involve a northerly LLJ, and these events tend to preferentially occur over the southeast quadrant of the main ridge of the SDC. To investigate the physical mechanisms acting to cause CI, idealized convection-permitting numerical simulations with a horizontal grid spacing of 1 km were conducted using CM1. The sounding used for initializing the model featured a strong northerly LLJ, with synoptic conditions resembling those in a previously postulated conceptual model of CI over the region, making it a canonical case study. Differential heating of the mountain caused by solar insolation in conjunction with the low-level northerly flow sets up a convergence line on the eastern slopes of the SDC. The southern portion of this line experiences significant reduction in convective inhibition, and CI occurs over the SDC southeast quadrant. Thesimulated storm soon acquires supercellular characteristics, as observed. Additional simulations with varying LLJ strength also show CI over the southeast quadrant. A simulation without background flow generated convergence over the ridgeline, with widespread CI across the entire ridgeline. A simulation with mid- and upper-tropospheric westerlies removed indicates that CI is minimally influenced by gravity waves. We conclude that the low-level jet is sufficient to focus convection initiation over the southeast quadrant of the ridge.

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Impact of enriched analyses on regional numerical forecasts over southeastern South America during SALLJEX

Revista Brasileira de Meteorologia ◽

10.1590/0102-778620100248 ◽

2014 ◽

Vol 29 (3) ◽

pp. 315-330

Author(s):

Yanina García Skabar ◽

Matilde Nicolini

Keyword(s):

Data Assimilation ◽

South America ◽

Atmospheric Modeling ◽

South American ◽

The South ◽

Low Level ◽

Positive Effects ◽

Accumulated Rainfall ◽

Low Level Jet ◽

The Impact

During the warm season 2002-2003, the South American Low-Level Jet Experiment (SALLJEX) was carried out in southeastern South America. Taking advantage of the unique database collected in the region, a set of analyses is generated for the SALLJEX period assimilating all available data. The spatial and temporal resolution of this new set of analyses is higher than that of analyses available up to present for southeastern South America. The aim of this paper is to determine the impact of assimilating data into initial fields on mesoscale forecasts in the region, using the Brazilian Regional Atmospheric Modeling System (BRAMS) with particular emphasis on the South American Low-Level Jet (SALLJ) structure and on rainfall forecasts. For most variables, using analyses with data assimilated as initial fields has positive effects on short term forecast. Such effect is greater in wind variables, but not significant in forecasts longer than 24 hours. In particular, data assimilation does not improve forecasts of 24-hour accumulated rainfall, but it has slight positive effects on accumulated rainfall between 6 and 12 forecast hours. As the main focus is on the representation of the SALLJ, the effect of data assimilation in its forecast was explored. Results show that SALLJ is fairly predictable however assimilating additional observation data has small impact on the forecast of SALLJ timing and intensity. The strength of the SALLJ is underestimated independently of data assimilation. However, Root mean square error (RMSE) and BIAS values reveal the positive effect of data assimilation up to 18-hours forecasts with a greater impact near higher topography.

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SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact

10.20944/preprints201904.0216.v1 ◽

2019 ◽

Author(s):

Nemesio Rodriguez-Fernandez ◽

Patricia de Rosnay ◽

Clement Albergel ◽

Philippe Richaume ◽

Filipe Aires ◽

...

Keyword(s):

Neural Network ◽

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Positive Impact ◽

Land Surface Model ◽

Surface Model ◽

Northern Asia ◽

The Impact

The assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised - Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically by training a neural network with SMOS brightness temperatures as input and H-TESSEL model SM simulations as reference. This can help the assimilation of SMOS information in several ways: (1) the neural network soil moisture (NNSM) data have a similar climatology to the model, (2) no global bias is present with respect to the model even if regional differences can exist. Experiments performing joint data assimilation (DA) of NNSM, 2 metre air temperature and relative humidity or NNSM-only DA are discussed. The resulting SM was evaluated against a large number of in situ measurements of SM obtaining similar results to those of the model with no assimilation, even if significant differences were found from site to site. In addition, atmospheric forecasts initialized with H-TESSEL runs (without DA) or with the analysed SM were compared to measure of the impact of the satellite information. Although, NNSM DA has an overall neutral impact in the forecast in the Tropics, a significant positive impact was found in other areas and periods, especially in regions with limited in situ information. The joint NNSM, T2m and RH2m DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T2m and RH2m, but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T2m and RH2m DA improves the forecast in April-September, while NNSM alone has a significant positive effect in July-September. Furthermore, forecasting skill maps show that SMOS NNSM improves the forecast in North America and in Northern Asia for up to 72 hours lead time.

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