NAM Model Forecasts of Warm-Season Quasi-Stationary Frontal Environments in the Central United States

2010 ◽  
Vol 25 (4) ◽  
pp. 1281-1292 ◽  
Author(s):  
Shih-Yu Wang ◽  
Adam J. Clark
Keyword(s):  
United States ◽  
Mesoscale Model ◽  
Convective Available Potential Energy ◽  
Warm Season ◽  
Correlation Coefficients ◽  
Precipitable Water ◽  
Wind Fields ◽  
Vapor Flux ◽  
Convective Systems ◽  
Central United States

Abstract Using a composite procedure, North American Mesoscale Model (NAM) forecast and observed environments associated with zonally oriented, quasi-stationary surface fronts for 64 cases during July–August 2006–08 were examined for a large region encompassing the central United States. NAM adequately simulated the general synoptic features associated with the frontal environments (e.g., patterns in the low-level wind fields) as well as the positions of the fronts. However, kinematic fields important to frontogenesis such as horizontal deformation and convergence were overpredicted. Surface-based convective available potential energy (CAPE) and precipitable water were also overpredicted, which was likely related to the overprediction of the kinematic fields through convergence of water vapor flux. In addition, a spurious coherence between forecast deformation and precipitation was found using spatial correlation coefficients. Composite precipitation forecasts featured a broad area of rainfall stretched parallel to the composite front, whereas the composite observed precipitation covered a smaller area and had a WNW–ESE orientation relative to the front, consistent with mesoscale convective systems (MCSs) propagating at a slight right angle relative to the thermal gradient. Thus, deficiencies in the NAM precipitation forecasts may at least partially result from the inability to depict MCSs properly. It was observed that errors in the precipitation forecasts appeared to lag those of the kinematic fields, and so it seems likely that deficiencies in the precipitation forecasts are related to the overprediction of the kinematic fields such as deformation. However, no attempts were made to establish whether the overpredicted kinematic fields actually contributed to the errors in the precipitation forecasts or whether the overpredicted kinematic fields were simply an artifact of the precipitation errors. Regardless of the relationship between such errors, recognition of typical warm-season environments associated with these errors should be useful to operational forecasters.

scholarly journals Early warm-season mesoscale convective systems dominate soil moisture–precipitation feedback for summer rainfall in central United States

2021 ◽  
Vol 118 (43) ◽  
pp. e2105260118
Author(s):  
Huancui Hu ◽  
L. Ruby Leung ◽  
Zhe Feng
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Land Surface ◽  
Warm Season ◽  
Summer Rainfall ◽  
Mesoscale Convective Systems ◽  
Rainfall Events ◽  
Convective Systems ◽  
Central United States ◽  
Mesoscale Convective

Land–atmosphere interactions play an important role in summer rainfall in the central United States, where mesoscale convective systems (MCSs) contribute to 30 to 70% of warm-season precipitation. Previous studies of soil moisture–precipitation feedbacks focused on the total precipitation, confounding the distinct roles of rainfall from different convective storm types. Here, we investigate the soil moisture–precipitation feedbacks associated with MCS and non-MCS rainfall and their surface hydrological footprints using a unique combination of these rainfall events in observations and land surface simulations with numerical tracers to quantify soil moisture sourced from MCS and non-MCS rainfall. We find that early warm-season (April to June) MCS rainfall, which is characterized by higher intensity and larger area per storm, produces coherent mesoscale spatial heterogeneity in soil moisture that is important for initiating summer (July) afternoon rainfall dominated by non-MCS events. On the other hand, soil moisture sourced from both early warm-season MCS and non-MCS rainfall contributes to lower-level atmospheric moistening favorable for upscale growth of MCSs at night. However, soil moisture sourced from MCS rainfall contributes to July MCS rainfall with a longer lead time because with higher intensity, MCS rainfall percolates into deeper soil that has a longer memory. Therefore, early warm-season MCS rainfall dominates soil moisture–precipitation feedback. This motivates future studies to examine the contribution of early warm-season MCS rainfall and associated soil moisture anomalies to predictability of summer rainfall in the major agricultural region of the central United States and other continental regions frequented by MCSs.

Corridors of Warm Season Precipitation in the Central United States

2006 ◽  
Vol 134 (9) ◽  
pp. 2297-2317 ◽  
Author(s):  
John D. Tuttle ◽  
Chris A. Davis
Keyword(s):  
United States ◽  
Environmental Factors ◽  
Large Scale ◽  
Warm Season ◽  
Extreme Case ◽  
Maximum Rainfall ◽  
Convective Systems ◽  
Precipitation Forecasting ◽  
Central United States ◽  
Mesoscale Convective

Abstract During the warm season in the central United States there often exists a corridor of precipitation where a succession of mesoscale convective systems (MCSs) follow similar paths lasting several days. The total cumulative rainfall within a corridor can be substantial while precipitation at nearby regions may be below normal. Understanding the nature of the corridors and the environmental factors important for their formation thus has important implications for quantitative precipitation forecasting and hydrological studies. In this study a U.S. national composite radar dataset and model-analyzed fields are used for the 1998–2002 warm seasons (July–August) to understand the properties of corridors and what environmental factors are important for determining when and where they develop. The analysis is restricted to a relatively narrow longitudinal band in the central United States (95°–100°W), a region where convection often intensifies and becomes highly organized. It is found that ∼68% of MCSs were members of a series and that corridors typically persist for 2–7 days with an extreme case lasting 13 days. Cumulative radar-derived maximum rainfall ranges from 8 to 50 cm, underscoring the fact that corridors can experience excessive rainfall. Combining radar with Rapid Update Cycle model kinematic and thermodynamic fields, 5-yr composites are presented and stratified according to the environmental conditions. While the corridors show the expected association with areas of enhanced CAPE and relatively strong northwesterly/westerly shear, the strongest association is with the northern terminus region of the nocturnal low-level jet (LLJ). Furthermore, the relative intensity of the rainfall is positively correlated with the strength of the LLJ. The LLJ is thought to play a role through enhanced convergence and lifting, moisture transport, and frontogenesis. In the five years analyzed, the large-scale environment varied considerably, but the role of the LLJ in the formation of corridors remained persistent.

scholarly journals Use of the Parcel Buoyancy Minimum (Bmin) to Diagnose Simulated Thermodynamic Destabilization. Part II: Composite Analysis of Mature MCS Environments

2014 ◽  
Vol 142 (3) ◽  
pp. 967-990 ◽  
Author(s):  
Stanley B. Trier ◽  
Christopher A. Davis ◽  
David A. Ahijevych ◽  
Kevin W. Manning
Keyword(s):  
Convective Available Potential Energy ◽  
Warm Season ◽  
Vertical Motion ◽  
Leading Edge ◽  
Composite Analysis ◽  
Convective Systems ◽  
Central United States ◽  
Adiabatic Cooling ◽  
Mesoscale Convective ◽  
Convection Initiation

Abstract Herein, the parcel buoyancy minimum (Bmin) defined in Part I of this two-part paper is used to examine physical processes influencing thermodynamic destabilization in environments of mature simulated mesoscale convective systems (MCSs). These convection-permitting simulations consist of twelve 24-h forecasts during two 6-day periods characterized by two different commonly occurring warm-season weather regimes that support MCSs over the central United States. A composite analysis of 22 MCS environments is performed where cases are stratified into surface-based (SB), elevated squall (ES), and elevated nonsquall (ENS) categories. A gradual reduction of lower-tropospheric Bmin to values indicative of small convection inhibition, occurring over horizontal scales >100 km from the MCS leading edge, is a common aspect of each category. These negative buoyancy decreases are most pronounced for the ES and ENS environments, in which convective available potential energy (CAPE) is greatest for air parcels originating above the surface. The implication is that the vertical structure of the mesoscale environment plays a key role in the evolution and sustenance of convection long after convection initiation and internal MCS circulations develop, particularly in elevated systems. Budgets of Bmin forcing are computed for the nocturnally maturing ES and ENS composites. Though warm advection occurs through the entire 1.5-km-deep layer comprising the vertical intersection of the largest environmental CAPE and smallest environmental Bmin magnitude, the net effect of terms involving vertical motion dominate the destabilization in both composites. These effects include humidity increases in air parcels due to vertical moisture advection and the adiabatic cooling of the environment above.

scholarly journals On the Climatology of Precipitable Water and Water Vapor Flux in the Mid-Atlantic Region of the United States

2015 ◽  
Vol 16 (1) ◽  
pp. 70-87 ◽  
Author(s):  
Young-Hee Ryu ◽  
James A. Smith ◽  
Elie Bou-Zeid
Keyword(s):  
United States ◽  
Water Vapor ◽  
Diurnal Cycle ◽  
Precipitable Water ◽  
The United States ◽  
Radiosonde Data ◽  
Atlantic Region ◽  
Water Vapor Flux ◽  
Vapor Flux ◽  
Diurnal Cycles

Abstract The seasonal and diurnal climatologies of precipitable water and water vapor flux in the mid-Atlantic region of the United States are examined. A new method of computing water vapor flux at high temporal resolution in an atmospheric column using global positioning system (GPS) precipitable water, radiosonde data, and velocity–azimuth display (VAD) wind profiles is presented. It is shown that water vapor flux exhibits striking seasonal and diurnal cycles and that the diurnal cycles exhibit rapid transitions over the course of the year. A particularly large change in the diurnal cycle of meridional water vapor flux between spring and summer seasons is found. These features of the water cycle cannot be resolved by twice-a-day radiosonde observations. It is also shown that precipitable water exhibits a pronounced seasonal cycle and a less pronounced diurnal cycle. There are large contrasts in the climatology of water vapor flux between precipitation and nonprecipitation conditions in the mid-Atlantic region. It is hypothesized that the seasonal transition of large-scale flow environments and the change in the degree of differential heating in the mountainous and coastal areas are responsible for the contrasting diurnal cycle between spring and summer seasons.

scholarly journals Assessment of bias-adjusted PM<sub>2.5</sub> air quality forecasts over the continental United States during 2007

2009 ◽  
Vol 2 (2) ◽  
pp. 1375-1406 ◽  
Author(s):  
D. Kang ◽  
R. Mathur ◽  
S. Trivikrama Rao
Keyword(s):  
United States ◽  
Kalman Filter ◽  
Air Quality ◽  
Recursive Algorithm ◽  
Warm Season ◽  
Correlation Coefficients ◽  
Rocky Mountain ◽  
Meteorological Model ◽  
Bias Adjustment ◽  
Air Quality Forecast

Abstract. To develop fine particular matter (PM2.5) air quality forecasts, a National Air Quality Forecast Capability (NAQFC) system, which linked NOAA's North American Mesoscale (NAM) meteorological model with EPA's Community Multiscale Air Quality (CMAQ) model, was deployed in the developmental mode over the continental United States during 2007. This study investigates the operational use of a bias-adjustment technique called the Kalman Filter Predictor approach for improving the accuracy of the PM2.5 forecasts at monitoring locations. The Kalman Filter Predictor bias-adjustment technique is a recursive algorithm designed to optimally estimate bias-adjustment terms using the information extracted from previous measurements and forecasts. The bias-adjustment technique is found to improve PM2.5 forecasts (i.e. reduced errors and increased correlation coefficients) for the entire year at almost all locations. The NAQFC tends to overestimate PM2.5 during the cool season and underestimate during the warm season in the eastern part of the continental US domain, but the opposite is true for the pacific coast. In the Rocky Mountain region, the NAQFC system overestimates PM2.5 for the whole year. The bias-adjustment forecasts can quickly (after 2–3 days' lag) adjust to reflect the transition from one regime to the other. The modest computational requirements and systematical improvements in forecast results across all seasons suggest that this technique can be easily adapted to perform bias-adjustment for real-time PM2.5 air quality forecasts.

scholarly journals Assessment of bias-adjusted PM<sub>2.5</sub> air quality forecasts over the continental United States during 2007

2010 ◽  
Vol 3 (1) ◽  
pp. 309-320 ◽  
Author(s):  
D. Kang ◽  
R. Mathur ◽  
S. Trivikrama Rao
Keyword(s):  
United States ◽  
Kalman Filter ◽  
Air Quality ◽  
Recursive Algorithm ◽  
Fine Particulate Matter ◽  
Warm Season ◽  
Correlation Coefficients ◽  
Meteorological Model ◽  
Bias Adjustment ◽  
Air Quality Forecast

Abstract. To develop fine particulate matter (PM2.5) air quality forecasts for the US, a National Air Quality Forecast Capability (NAQFC) system, which linked NOAA's North American Mesoscale (NAM) meteorological model with EPA's Community Multiscale Air Quality (CMAQ) model, was deployed in the developmental mode over the continental United States during 2007. This study investigates the operational use of a bias-adjustment technique called the Kalman Filter Predictor approach for improving the accuracy of the PM2.5 forecasts at monitoring locations. The Kalman Filter Predictor bias-adjustment technique is a recursive algorithm designed to optimally estimate bias-adjustment terms using the information extracted from previous measurements and forecasts. The bias-adjustment technique is found to improve PM2.5 forecasts (i.e. reduced errors and increased correlation coefficients) for the entire year at almost all locations. The NAQFC tends to overestimate PM2.5 during the cool season and underestimate during the warm season in the eastern part of the continental US domain, but the opposite is true for the Pacific Coast. In the Rocky Mountain region, the NAQFC system overestimates PM2.5 for the whole year. The bias-adjusted forecasts can quickly (after 2–3 days' lag) adjust to reflect the transition from one regime to the other. The modest computational requirements and systematic improvements in forecast outputs across all seasons suggest that this technique can be easily adapted to perform bias adjustment for real-time PM2.5 air quality forecasts.

scholarly journals A Dual-Polarization Radar Synthetic QPE for Operations

2020 ◽  
Vol 21 (11) ◽  
pp. 2507-2521 ◽  
Author(s):  
Jian Zhang ◽  
Lin Tang ◽  
Stephen Cocks ◽  
Pengfei Zhang ◽  
Alexander Ryzhkov ◽  
...  
Keyword(s):  
United States ◽  
Scattered Light ◽  
Warm Season ◽  
Dual Polarization ◽  
Specific Attenuation ◽  
Central United States ◽  
Precipitation Estimation ◽  
Systematic Biases ◽  
Differential Reflectivity ◽  
Light Precipitation

AbstractA new dual-polarization (DP) radar synthetic quantitative precipitation estimation (QPE) product was developed using a combination of specific attenuation A, specific differential phase KDP, and reflectivity Z to calculate the precipitation rate R. Specific attenuation has advantages of being insensitive to systematic biases in Z and differential reflectivity ZDR due to partial beam blockage, attenuation, and calibration while more linearly related to R than other radar variables. However, the R(A) relationship is not applicable in areas containing ice. Therefore, the new DP QPE applies R(A) in areas where radar is observing pure rain, R(KDP) in regions potentially containing hail, and R(Z) elsewhere. Further, an evaporation correction was applied to minimize false light precipitation related to virga. The new DP QPE was evaluated in real time over the conterminous United States and showed significant improvements over previous radar QPE techniques that were based solely on R(Z) relationships. The improvements included reduced dry biases in heavy to extreme precipitation during the warm season. The new DP QPE also reduced errors and spatial discontinuities in regions impacted by partial beam blockage. Further, the new DP QPE reduced wet bias for scattered light precipitation in the southwest and north central United States where there is significant boundary layer evaporation.

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