The Effect of Upstream Convection on Downstream Precipitation

Abstract Operational forecasters in the southeast and mid-Atlantic regions of the United States have noted a positive quantitative precipitation forecast (QPF) bias in numerical weather prediction (NWP) model forecasts downstream of some organized, cold-season convective systems. Examination of cold-season cases in which model QPF guidance exhibited large errors allowed identification of two representative cases for detailed analysis. The goals of the case study analyses are to (i) identify physical mechanisms through which the upstream convection (UC) alters downstream precipitation amounts, (ii) determine why operational models are challenged to provide accurate guidance in these situations, and (iii) suggest future research directions that would improve model forecasts in these situations and allow forecasters to better anticipate such events. Two primary scenarios are identified during which downstream precipitation is altered in the presence of UC for the study region: (i) a fast-moving convective (FC) scenario in which organized convective systems oriented parallel to the lower-tropospheric flow are progressive relative to the parent synoptic system, and appear to disrupt poleward moisture transport, and (ii) a situation characterized by slower-moving convection (SC) relative to the parent system. Analysis of a representative FC case indicated that moisture consumption, stabilization via convective overturning, and modification of the low-level flow to a more westerly direction in the postconvective environment all appear to contribute to the reduction of downstream precipitation. In the FC case, operational Eta Model forecasts moved the organized UC too slowly, resulting in an overestimate of downstream moisture transport. A 4-km explicit convection model forecast from the Weather Research and Forecasting model produced a faster-moving upstream convective system and improved downstream QPF. In contrast to the FC event, latent heat release in the primary rainband is shown to enhance the low-level jet ahead of the convection in the SC case, thereby increasing moisture transport into the downstream region. A negative model QPF bias was observed in Eta Model forecasts for the SC event. Suggestions are made for precipitation forecasting in UC situations, and implications for NWP model configuration are discussed.

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Predictive Capability of a High-Resolution Hydrometeorological Forecasting Framework Coupling WRF Cycling 3DVAR and Continuum

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0219.1 ◽

2019 ◽

Vol 20 (7) ◽

pp. 1307-1337 ◽

Cited By ~ 5

Author(s):

Martina Lagasio ◽

Francesco Silvestro ◽

Lorenzo Campo ◽

Antonio Parodi

Keyword(s):

Flash Flood ◽

Weather Prediction ◽

Precipitation Forecast ◽

Distributed Hydrological Model ◽

Predictive Capability ◽

Flood Prediction ◽

Convective Systems ◽

Data Intensive ◽

Streamflow Forecasts ◽

Nwp Model

Abstract The typical complex orography of the Mediterranean coastal areas support the formation of the so-called back-building mesoscale convective systems (MCS) producing torrential rainfall often resulting in flash floods. As these events are usually very small-scaled and localized, they are hardly predictable from a hydrometeorological standpoint, frequently causing a significant amount of fatalities and socioeconomic damage. Liguria, a northwestern Italian region, is characterized by small catchments with very short hydrological response time and is thus extremely prone to the impacts of back-building MCSs. Indeed, Liguria has been hit by three intense back-building MCSs between 2011 and 2014, causing a total death toll of 20 people and several hundred millions of euros of damages. Consequently, it is necessary to use hydrometeorological forecasting frameworks coupling the finescale numerical weather prediction (NWP) outputs with rainfall–runoff models to provide timely and accurate streamflow forecasts. Concerning the aforementioned back-building MCS episodes that recently occurred in Liguria, this work assesses the predictive capability of a hydrometeorological forecasting framework composed by a kilometer-scale cloud-resolving NWP model (WRF), including a 6-h cycling 3DVAR assimilation of radar reflectivity and conventional weather stations data, a rainfall downscaling model [Rainfall Filtered Autoregressive Model (RainFARM)], and a fully distributed hydrological model (Continuum). A rich portfolio of WRF 3DVAR direct and indirect reflectivity operators has been explored to drive the meteorological component of the proposed forecasting framework. The results confirm the importance of rapidly refreshing and data intensive 3DVAR for improving the quantitative precipitation forecast, and, subsequently, the flash flood prediction in cases of back-building MCS events.

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Present and Past Sea Surface Temperatures: A Recipe for Better Seasonal Climate Forecasts

Weather and Forecasting ◽

10.1175/waf-d-19-0241.1 ◽

2020 ◽

Vol 35 (4) ◽

pp. 1221-1234

Author(s):

Matthew B. Switanek ◽

Joseph J. Barsugli ◽

Michael Scheuerer ◽

Thomas M. Hamill

Keyword(s):

United States ◽

Surface Temperature ◽

Sea Surface Temperature ◽

The United States ◽

Cold Season ◽

Sea Surface ◽

Precipitation Forecast ◽

The North ◽

Model Ensembles ◽

Tropical Sea

AbstractMonthly tropical sea surface temperature (SST) data are used as predictors to make statistical forecasts of cold season (November–March) precipitation and temperature for the contiguous United States. Through the use of the combined-lead sea surface temperature (CLSST) model, predictive information is discovered not just in recent SSTs but also from SSTs up to 18 months prior. We find that CLSST cold season forecast anomaly correlation skill is higher than that of the North American Multimodel Ensemble (NMME) and the SEAS5 model from the European Centre for Medium-Range Weather Forecasts (ECMWF) when averaged over the United States for both precipitation and 2-m air temperature. The precipitation forecast skill obtained by CLSST in parts of the Intermountain West is of particular interest because of its implications for water resources. In those regions, CLSST dramatically improves the skill over that of the dynamical model ensembles, which can be attributed to a robust statistical response of precipitation in this region to SST anomalies from the previous year in the tropical Pacific.

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Atmospheric Moisture Transport over the United States and Mexico as Evaluated in the NCEP Regional Reanalysis

Journal of Hydrometeorology ◽

10.1175/jhm452.1 ◽

2005 ◽

Vol 6 (5) ◽

pp. 710-728 ◽

Cited By ~ 75

Author(s):

Kingtse C. Mo ◽

Muthuvel Chelliah ◽

Marco L. Carrera ◽

R. Wayne Higgins ◽

Wesley Ebisuzaki

Keyword(s):

United States ◽

Gulf Of Mexico ◽

Great Plains ◽

Moisture Transport ◽

Gulf Of California ◽

Phase Relationship ◽

The United States ◽

Low Level ◽

Low Level Jet ◽

Regional Reanalysis

Abstract The large-scale atmospheric hydrologic cycle over the United States and Mexico derived from the 23-yr NCEP regional reanalysis (RR) was evaluated by comparing the RR products with satellite estimates, independent sounding data, and the operational Eta Model three-dimensional variational data assimilation (3DVAR) system (EDAS). In general, the winter atmospheric transport and precipitation are realistic. The climatology and interannual variability of the Pacific, subtropical jet streams, and low-tropospheric moisture transport are well captured. During the summer season, the basic features and the evolution of the North American monsoon (NAM) revealed by the RR compare favorably with observations. The RR also captures the out-of-phase relationship of precipitation as well as the moisture flux convergence between the central United States and the Southwest. The RR is able to capture the zonal easterly Caribbean low-level jet (CALLJ) and the meridional southerly Great Plains low-level jet (GPLLJ). Together, they transport copious moisture from the Caribbean to the Gulf of Mexico and from the Gulf of Mexico to the Great Plains, respectively. The RR systematically overestimates the meridional southerly Gulf of California low-level jet (GCLLJ). A comparison with observations suggests that the meridional winds from the RR are too strong, with the largest differences centered over the northern Gulf of California. The strongest winds over the Gulf in the RR extend above 700 hPa, while the operational EDAS and station soundings indicate that the GCLLJ is confined to the boundary layer.

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Mesoscale Moisture Transport by the Low-Level Jet during the IHOP Field Experiment

Monthly Weather Review ◽

10.1175/2008mwr2421.1 ◽

2008 ◽

Vol 136 (10) ◽

pp. 3781-3795 ◽

Cited By ~ 11

Author(s):

Edward I. Tollerud ◽

Fernando Caracena ◽

Steven E. Koch ◽

Brian D. Jamison ◽

R. Michael Hardesty ◽

...

Keyword(s):

Great Plains ◽

Cross Sections ◽

Moisture Transport ◽

Large Scale ◽

Wrf Model ◽

The United States ◽

Small Scale ◽

Low Level ◽

Convective Scale ◽

Low Level Jet

Previous studies of the low-level jet (LLJ) over the central Great Plains of the United States have been unable to determine the role that mesoscale and smaller circulations play in the transport of moisture. To address this issue, two aircraft missions during the International H2O Project (IHOP_2002) were designed to observe closely a well-developed LLJ over the Great Plains (primarily Oklahoma and Kansas) with multiple observation platforms. In addition to standard operational platforms (most important, radiosondes and profilers) to provide the large-scale setting, dropsondes released from the aircraft at 55-km intervals and a pair of onboard lidar instruments—High Resolution Doppler Lidar (HRDL) for wind and differential absorption lidar (DIAL) for moisture—observed the moisture transport in the LLJ at greater resolution. Using these observations, the authors describe the multiscalar structure of the LLJ and then focus attention on the bulk properties and effects of scales of motion by computing moisture fluxes through cross sections that bracket the LLJ. From these computations, the Reynolds averages within the cross sections can be computed. This allow an estimate to be made of the bulk effect of integrated estimates of the contribution of small-scale (mesoscale to convective scale) circulations to the overall transport. The performance of the Weather Research and Forecasting (WRF) Model in forecasting the intensity and evolution of the LLJ for this case is briefly examined.

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Near-Surface Thermodynamic Sensitivities in Simulated Extreme-Rain-Producing Mesoscale Convective Systems

Monthly Weather Review ◽

10.1175/mwr-d-16-0255.1 ◽

2017 ◽

Vol 145 (6) ◽

pp. 2177-2200 ◽

Cited By ~ 13

Author(s):

Russ S. Schumacher ◽

John M. Peters

Keyword(s):

Water Vapor ◽

Initial Conditions ◽

Weather Prediction ◽

Mesoscale Convective Systems ◽

Cold Pool ◽

Near Surface ◽

Low Level ◽

Convective Systems ◽

Mesoscale Convective ◽

Outflow Boundary

Abstract This study investigates the influences of low-level atmospheric water vapor on the precipitation produced by simulated warm-season midlatitude mesoscale convective systems (MCSs). In a series of semi-idealized numerical model experiments using initial conditions gleaned from composite environments from observed cases, small increases in moisture were applied to the model initial conditions over a layer either 600 m or 1 km deep. The precipitation produced by the MCS increased with larger moisture perturbations as expected, but the rainfall changes were disproportionate to the magnitude of the moisture perturbations. The experiment with the largest perturbation had a water vapor mixing ratio increase of approximately 2 g kg−1 over the lowest 1 km, corresponding to a 3.4% increase in vertically integrated water vapor, and the area-integrated MCS precipitation in this experiment increased by nearly 60% over the control. The locations of the heaviest rainfall also changed in response to differences in the strength and depth of the convectively generated cold pool. The MCSs in environments with larger initial moisture perturbations developed stronger cold pools, and the convection remained close to the outflow boundary, whereas the convective line was displaced farther behind the outflow boundary in the control and the simulations with smaller moisture perturbations. The high sensitivity of both the amount and location of MCS rainfall to small changes in low-level moisture demonstrates how small moisture errors in numerical weather prediction models may lead to large errors in their forecasts of MCS placement and behavior.

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Tornado Damage Rating Probabilities Derived from WSR-88D Data

Weather and Forecasting ◽

10.1175/waf-d-17-0004.1 ◽

2017 ◽

Vol 32 (4) ◽

pp. 1509-1528 ◽

Cited By ~ 17

Author(s):

Richard L. Thompson ◽

Bryan T. Smith ◽

Jeremy S. Grams ◽

Andrew R. Dean ◽

Joseph C. Picca ◽

...

Keyword(s):

United States ◽

Great Plains ◽

Probability Distributions ◽

Rotational Velocity ◽

The United States ◽

Low Level ◽

Convective Systems ◽

Severe Thunderstorms ◽

Damage Indicators ◽

Tornado Damage

Abstract Previous work with observations from the NEXRAD (WSR-88D) network in the United States has shown that the probability of damage from a tornado, as represented by EF-scale ratings, increases as low-level rotational velocity increases. This work expands on previous studies by including reported tornadoes from 2014 to 2015, as well as a robust sample of nontornadic severe thunderstorms [≥1-in.- (2.54 cm) diameter hail, thunderstorm wind gusts ≥ 50 kt (25 m s−1), or reported wind damage] with low-level cyclonic rotation. The addition of the nontornadic sample allows the computation of tornado damage rating probabilities across a spectrum of organized severe thunderstorms represented by right-moving supercells and quasi-linear convective systems. Dual-polarization variables are used to ensure proper use of velocity data in the identification of tornadic and nontornadic cases. Tornado damage rating probabilities increase as low-level rotational velocity Vrot increases and circulation diameter decreases. The influence of height above radar level (or range from radar) is less obvious, with a muted tendency for tornado damage rating probabilities to increase as rotation (of the same Vrot magnitude) is observed closer to the ground. Consistent with previous work on gate-to-gate shear signatures such as the tornadic vortex signature, easily identifiable rotation poses a greater tornado risk compared to more nebulous areas of cyclonic azimuthal shear. Additionally, tornado probability distributions vary substantially (for similar sample sizes) when comparing the southeast United States, which has a high density of damage indicators, to the Great Plains, where damage indicators are more sparse.

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Observational Analysis of the Predictability of Mesoscale Convective Systems

Weather and Forecasting ◽

10.1175/waf1012.1 ◽

2007 ◽

Vol 22 (4) ◽

pp. 813-838 ◽

Cited By ~ 33

Author(s):

Israel L. Jirak ◽

William R. Cotton

Keyword(s):

United States ◽

Wind Shear ◽

Vertical Wind Shear ◽

The United States ◽

Vertical Wind ◽

Mesoscale Convective Systems ◽

Convective Initiation ◽

Low Level ◽

Convective Systems ◽

Mesoscale Convective

Abstract Mesoscale convective systems (MCSs) have a large influence on the weather over the central United States during the warm season by generating essential rainfall and severe weather. To gain insight into the predictability of these systems, the precursor environments of several hundred MCSs across the United States were reviewed during the warm seasons of 1996–98. Surface analyses were used to identify initiating mechanisms for each system, and North American Regional Reanalysis (NARR) data were used to examine the environment prior to MCS development. Similarly, environments unable to support organized convective systems were also investigated for comparison with MCS precursor environments. Significant differences were found between environments that support MCS development and those that do not support convective organization. MCSs were most commonly initiated by frontal boundaries; however, features that enhance convective initiation are often not sufficient for MCS development, as the environment needs also to be supportive for the development and organization of long-lived convective systems. Low-level warm air advection, low-level vertical wind shear, and convective instability were found to be the most important parameters in determining whether concentrated convection would undergo upscale growth into an MCS. Based on these results, an index was developed for use in forecasting MCSs. The MCS index assigns a likelihood of MCS development based on three terms: 700-hPa temperature advection, 0–3-km vertical wind shear, and the lifted index. An evaluation of the MCS index revealed that it exhibits features consistent with common MCS characteristics and is reasonably accurate in forecasting MCSs, especially given that convective initiation has occurred, offering the possibility of usefulness in operational forecasting.

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Performance of IMD multi-model ensemble and WRF (ARW) model for sub-basin wise rainfall forecast during monsoon 2012

MAUSAM ◽

10.54302/mausam.v67i2.1298 ◽

2021 ◽

Vol 67 (2) ◽

pp. 323-332

Author(s):

ASHOK KUMAR DAS ◽

SURINDER KAUR

Keyword(s):

Prediction Models ◽

Weather Prediction ◽

India Meteorological Department ◽

Precipitation Forecast ◽

False Alarms ◽

Model Ensemble ◽

Rainfall Forecast ◽

Quantitative Precipitation Forecast ◽

Arw Model ◽

Nwp Model

The Numerical Weather Prediction models, Multi-model Ensemble (MME) (27 km × 27 km) and WRF (ARW) (9 km × 9 km) operationally run by India Meteorological Department (IMD) have been utilized to estimate sub-basin wise rainfall forecast. The sub-basin wise operational Quantitative Precipitation Forecast (QPF) have been issued by 10 field offices named Flood Meteorological Offices (FMOs) of IMD located at different flood prone areas of the country. The daily sub-basin wise NWP model rainfall forecast for 122 sub basins under these 10 FMOs for the flood season 2012 have been estimated on operational basis which are used by forecasters at FMOs as a guidance for the issue of operational sub-basin QPF for flood forecasting purposes. The performance of the MME and WRF (ARW) models rainfall at the sub-basin level have been studied in detail. The performance of WRF (ARW) and MME models is compared in the heavy rainfall case over the river basins (Mahanadi etc.) falls under FMO, Bhubaneswar and it is found that WRF (ARW) model gives better result than MME. It is also found that performance of WRF (ARW) is little better than MME when compared over all the flood prone river sub basins of India. For high rainfall categories (51-100, >100 mm), generally these leads to floods, the success rate of model rainfall forecasts are less and false alarms are more. The NWP models are able to capture the rainfall events but there is difference in magnitudes of sub basin wise rainfall estimates.

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Low-level windshear associated with atmospheric boundary layer jets – Case studies

Atmósfera ◽

10.20937/atm.52866 ◽

2020 ◽

Vol 34 (4) ◽

pp. 461-490

Author(s):

P. W. Chan ◽

K. K. Hon ◽

Q. S. Li

Keyword(s):

Boundary Layer ◽

Atmospheric Boundary Layer ◽

Southern China ◽

Weather Prediction ◽

Northeast Monsoon ◽

Jet Streams ◽

Easterly Wind ◽

Low Level ◽

Reasonable Prediction ◽

Nwp Model

Jet streams in the atmospheric boundary layer may lead to hazardous weather over southern China. In this paper, the jet-related low-level windshear to be encountered by an aircraft is documented. Two typical cases under the northeast monsoon regime are considered, namely, easterly jet disrupted by the mountains to the south of Hong Kong International Airport, and outbreak of monsoon surge that produces a low-level northeasterly jet. The Doppler Light Detection and Ranging (LIDAR) systems are found to capture the corresponding windshear features very well, e.g., consistent with pilot reports and flight data. They are useful in providing timely alert to the aircraft. In particular, the LIDAR captures a double jet structure in the atmospheric boundary layer for the easterly wind case, which has not been reported in the literature before. The physical mechanism for the occurrence of the double jet is yet to be revealed. Moreover, the performance of a high spatial resolution (200 m) numerical weather prediction (NWP) model in predicting the jet and the associated low-level windshear is studied. The model is found to provide reasonable prediction of the windshear features at a few hours ahead, and, for the cases studied, shows skills in providing timely alerts to the aircraft.

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Estimates of Cn2 from Numerical Weather Prediction Model Output and Comparison with Thermosonde Data

Journal of Applied Meteorology and Climatology ◽

10.1175/2010jamc2350.1 ◽

2010 ◽

Vol 49 (8) ◽

pp. 1742-1755 ◽

Cited By ~ 13

Author(s):

Rod Frehlich ◽

Robert Sharman ◽

Francois Vandenberghe ◽

Wei Yu ◽

Yubao Liu ◽

...

Keyword(s):

Spatial Structure ◽

Structure Function ◽

Numerical Weather Prediction ◽

Weather Prediction ◽

The United States ◽

Spatial Filter ◽

Structure Functions ◽

Model Output ◽

Numerical Weather ◽

Nwp Model

Abstract Area-averaged estimates of Cn2 from high-resolution numerical weather prediction (NWP) model output are produced from local estimates of the spatial structure functions of refractive index with corrections for the inherent smoothing and filtering effects of the underlying NWP model. The key assumptions are the existence of a universal statistical description of small-scale turbulence and a locally universal spatial filter for the NWP model variables. Under these assumptions, spatial structure functions of the NWP model variables can be related to the structure functions of the atmospheric variables and extended to the smaller underresolved scales. The shape of the universal spatial filter is determined by comparisons of model structure functions with the climatological spatial structure function determined from an archive of aircraft data collected in the upper troposphere and lower stratosphere. This method of computing Cn2 has an important advantage over more traditional methods that are based on vertical differences because the structure function–based estimates avoid reference to the turbulence outer length scale. To evaluate the technique, NWP model–derived structure-function estimates of Cn2 are compared with nighttime profiles of Cn2 derived from temperature structure-function sensors attached to a rawinsonde (thermosonde) near Holloman Air Force Base in the United States.

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