Potential Vorticity (PV) Thinking in Operations: The Utility of Nonconservation

2008 ◽  
Vol 23 (1) ◽  
pp. 168-182 ◽  
Author(s):  
Michael J. Brennan ◽  
Gary M. Lackmann ◽  
Kelly M. Mahoney
Keyword(s):  
Potential Vorticity ◽  
High Frequency ◽  
Weather Prediction ◽  
Precipitation Amount ◽  
Extratropical Cyclones ◽  
Model Output ◽  
Case Examples ◽  
Convective Systems ◽  
Low Level Jets ◽  
Nwp Model

Abstract The use of the potential vorticity (PV) framework by operational forecasters is advocated through case examples that demonstrate its utility for interpreting and evaluating numerical weather prediction (NWP) model output for weather systems characterized by strong latent heat release (LHR). The interpretation of the dynamical influence of LHR is straightforward in the PV framework; LHR can lead to the generation of lower-tropospheric cyclonic PV anomalies. These anomalies can be related to meteorological phenomena including extratropical cyclones and low-level jets (LLJs), which can impact lower-tropospheric moisture transport. The nonconservation of PV in the presence of LHR results in a modification of the PV distribution that can be identified in NWP model output and evaluated through a comparison with observations and high-frequency gridded analyses. This methodology, along with the application of PV-based interpretation, can help forecasters identify aspects of NWP model solutions that are driven by LHR; such features are often characterized by increased uncertainty due to difficulties in model representation of precipitation amount and latent heating distributions, particularly for convective systems. Misrepresentation of the intensity and/or distribution of LHR in NWP model forecasts can generate errors that propagate through the model solution with time, potentially degrading the representation of cyclones and LLJs in the model forecast. The PV framework provides human forecasters with a means to evaluate NWP model forecasts in a way that facilitates recognition of when and how value may be added by modifying NWP guidance. This utility is demonstrated in case examples of coastal extratropical cyclogenesis and LLJ enhancement. Information is provided regarding tools developed for applying PV-based techniques in an operational setting.

scholarly journals Potential Vorticity Diagnostics to Quantify Effects of Latent Heating in Extratropical Cyclones. Part I: Methodology

2017 ◽  
Vol 74 (11) ◽  
pp. 3567-3590 ◽  
Author(s):  
Dominik Büeler ◽  
Stephan Pfahl
Keyword(s):  
Potential Vorticity ◽  
Weather Prediction ◽  
Baroclinic Instability ◽  
Numerical Weather Prediction Model ◽  
Cloud Formation ◽  
Extratropical Cyclones ◽  
Latent Heating ◽  
Strongly Coupled ◽  
Cyclone Intensity ◽  
Diabatic Processes

Abstract Extratropical cyclones develop because of baroclinic instability, but their intensification is often substantially amplified by diabatic processes, most importantly, latent heating (LH) through cloud formation. Although this amplification is well understood for individual cyclones, there is still need for a systematic and quantitative investigation of how LH affects cyclone intensification in different, particularly warmer and moister, climates. For this purpose, the authors introduce a simple diagnostic to quantify the contribution of LH to cyclone intensification within the potential vorticity (PV) framework. The two leading terms in the PV tendency equation, diabatic PV modification and vertical advection, are used to derive a diagnostic equation to explicitly calculate the fraction of a cyclone’s positive lower-tropospheric PV anomaly caused by LH. The strength of this anomaly is strongly coupled to cyclone intensity and the associated impacts in terms of surface weather. To evaluate the performance of the diagnostic, sensitivity simulations of 12 Northern Hemisphere cyclones with artificially modified LH are carried out with a numerical weather prediction model. Based on these simulations, it is demonstrated that the PV diagnostic captures the mean sensitivity of the cyclones’ PV structure to LH as well as parts of the strong case-to-case variability. The simple and versatile PV diagnostic will be the basis for future climatological studies of LH effects on cyclone intensification.

scholarly journals The Effect of Upstream Convection on Downstream Precipitation

2007 ◽  
Vol 22 (2) ◽  
pp. 255-277 ◽  
Author(s):  
Kelly M. Mahoney ◽  
Gary M. Lackmann
Keyword(s):  
Moisture Transport ◽  
Weather Prediction ◽  
The United States ◽  
Cold Season ◽  
Precipitation Forecast ◽  
Study Region ◽  
Low Level ◽  
Convective Systems ◽  
Eta Model ◽  
Nwp Model

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.

scholarly journals Influence of Tropical Waves on the Lifecycle of Mesoscale Convective Systems over West Africa

2021 ◽  
Author(s):  
Marlon Maranan ◽  
Andreas Schlueter ◽  
Andreas H. Fink ◽  
Peter Knippertz
Keyword(s):  
West Africa ◽  
High Frequency ◽  
Rainfall Variability ◽  
Weather Prediction ◽  
Mesoscale Convective Systems ◽  
Convection Current ◽  
Maturity Stage ◽  
Convective Systems ◽  
Tropical Waves ◽  
Mesoscale Convective

<p>Rainfall variability over West Africa remains a major challenge for numerical weather prediction (NWP). Due to the largely stochastic and sub-grid nature of tropical convection, current NWP models still fail to provide reliable precipitation forecasts – even for a 1-day leadtime – and are barely more skillful than climatology-based forecasts. Thus, several recent studies have investigated the presumably more predictable influence of tropical waves on environmental conditions for convection and found distinct and coherent (thermo-)dynamical patterns depending on the type and phase of the wave. Of particular interest in this context is the interaction of the wave with the lifecycle of usually westward propagating mesoscale convective systems (MCSs), which are the major providers of rain in the region and can occasionally even lead to flooding. The exact mechanisms and strength of this interaction are still not entirely known.</p><p>This study combines two recent datasets in a novel way in order to systematically investigate the influence of tropical waves on MCS characteristics and lifecycle. First, MCSs are tracked within northern tropical Africa (20°W-30°E / 2°-15°N) over an 11-year period during the West African rainy season (April-October) using infrared brightness temperature fields provided by the Spinning enhanced visible and infrared imager (SEVIRI). Second, tropical waves are isolated by applying a filtering method in the wave-frequency domain to precipitation data of the Tropical Rainfall Measuring Mission (TRMM) within the 5°-15°N latitude band for the same target period. By combining the two datasets in space and time, the magnitude and phase of each wave is known at every timestep of the MCS tracks, which enables a systematic investigation of MCS characteristics as a function of wave properties.</p><p>Preliminary results suggest that long-lived MCSs (lifetime ≥ 12h) frequently couple with the “wet” phase of high-frequency tropical waves, in particular Kelvin, eastward inertia-gravity (EIG), and African easterly waves (AEW). Showing an enhanced occurrence frequency of MCS initiation, the wet phase of AEWs appears to have strong modulation capabilities during the genesis stage and further accompanies these long-lived MCSs during their entire lifetime. In the case of Kelvin waves and EIGs, the wet phase overlaps only with the intensification and maturity stage of these MCSs as a consequence of opposite directions of movement. Similar coupling patterns also exist for mixed Rossby gravity waves (MRGs), although to a weaker extent. Furthermore, no consistent coupling tendencies with long-lived MCSs are evident for low-frequency waves (Madden-Julian Oscillation (MJO), equatorial Rossby wave (ER)), arguably since they act on larger spatio-temporal scales. For short-lived MCSs (lifetime < 6h), the coupling with high-frequency waves is substantially weaker.</p><p>In the future we will also address potential influences of wave-wave interactions on MCSs as well as potential differences in coupling mechanisms between the Guinea Coast region and the Sahel farther north. With increasing efforts in the prediction of tropical waves, this study has the potential to aid the short-term forecasting of MCS development and its lifecycle. This can be of particular importance for the anticipation of extreme rainfall events and subsequent risk assessment in West Africa.</p>

The Use of Structure Functions and Spectra from Numerical Model Output to Determine Effective Model Resolution

2008 ◽  
Vol 136 (4) ◽  
pp. 1537-1553 ◽  
Author(s):  
Rod Frehlich ◽  
Robert Sharman
Keyword(s):  
Spatial Structure ◽  
Spatial Statistics ◽  
Weather Prediction ◽  
Structure Functions ◽  
Effective Model ◽  
Model Output ◽  
Model Resolution ◽  
Nwp Model ◽  
Spatial Spectra ◽  
Aircraft Data

Abstract The effective model resolution of three numerical weather prediction (NWP) models is determined from analyses of spatial structure functions and spatial spectra. In this paper, the effective resolution is defined as the dimensions of the rectangular spatial filter that describes the net effect of all of the NWP model’s numerical filtering and smoothing effects. These effects are determined by comparison of spatial statistics of the NWP model output with statistical climatologies derived from aircraft data for the upper troposphere and lower stratosphere. The comparisons are based on both spatial structure functions and spatial spectra. The structure function approach has fewer assumptions and fewer numerical artifacts. Accurate estimates of NWP effective model resolution require a robust climatology of the spatial statistics, which are a function of latitude and location, such as over mountainous regions. An artifact in the climatology of the velocity statistics resulting from mountain waves is identified from NWP model output and corroborated with research aircraft data, which has not been previously observed in global statistical climatologies.

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

2010 ◽  
Vol 49 (8) ◽  
pp. 1742-1755 ◽  
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.

scholarly journals An algorithm for detecting the Chesapeake Bay breeze from mesoscale NWP model output

2021 ◽  
Keyword(s):  
Chesapeake Bay ◽  
Prediction Models ◽  
Weather Prediction ◽  
Wrf Model ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Model Output ◽  
Detection Algorithms ◽  
Bay Breeze ◽  
Nwp Model

Abstract A novel algorithm is developed for detecting and classifying the Chesapeake Bay breeze and similar water-body breezes in output from mesoscale numerical weather prediction models. To assess the generality of the new model-based detection algorithm (MBDA), it is tested on simulations from the Weather Research and Forecasting (WRF) model and on analyses and forecasts from the High Resolution Rapid Refresh (HRRR) model. The MBDA outperforms three observation-based detection algorithms (OBDAs) when applied to the same model output. Additionally, by defining the onshore wind directions based on model land-use data, not on the actual geography of the region of interest, performance of the OBDAs with model output can be improved. Although simulations by the WRF model were used to develop the new MBDA, it performed best when applied to HRRR analyses. The generality of the MBDA is promising, and additional tuning of its parameters might improve it further.

Developing a Sub-seasonal Forecasting System for Hydropower Reservoirs in Scotland 

2021 ◽  
Author(s):  
Robert M. Graham ◽  
Jethro Browell ◽  
Douglas Bertram ◽  
Christopher J. White
Keyword(s):  
Water Management ◽  
Weather Forecasting ◽  
Cost Model ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Added Value ◽  
Model Output ◽  
Adverse Weather ◽  
Nwp Model ◽  
Hydropower Reservoirs

<p>Inflow forecasts play an essential role in the management of hydropower reservoirs. Forecasts help operators to mitigate flood risks, meet environmental requirements, and maximise the value of power generated. In Scotland, operational inflow forecasts for hydropower facilities are typically limited in range to 2 weeks ahead, which marks the predictability barrier of deterministic weather forecasts. Extending the horizon of these forecasts may allow operators to take more proactive responses to risks of adverse weather conditions, thereby improving water management and increasing profits.</p><p>This study outlines a method of producing skilful probabilistic inflow forecasts for hydropower reservoirs on sub-seasonal timescales (up to 6-weeks ahead), directly from Numerical Weather Prediction (NWP) model output. Using a case study site of a large hydropower reservoir in the Scottish Highlands, we use the European Centre for Medium-range Weather Forecasting (ECMWF) extended-range forecast to create probabilistic inflow forecasts for the reservoir. Inflow forecasts are derived by training a linear regression model for the observed inflow onto the NWP precipitation, and subsequently applying post-processing techniques from Ensemble Model Output Statistics.</p><p>We show that the inflow forecasts hold fair skill relative to climatology up to six weeks ahead. Average inflow forecasts for the period 1-35 days ahead hold good skill relative to climatology, and are comparably skilful to an average inflow forecast for the period 8-14 days ahead. Forecasts are more skilful in winter than summer, which is consistent with physical teleconnections from the tropics that operate on sub-seasonal timescales.</p><p>We further apply a stylised cost model that demonstrates the potential value of these forecasts through improved water management. The stylised cost model indicates that the sub-seasonal probabilistic inflow forecast are sufficiently reliable to improve decision making and deliver added value across all forecast horizons up to six weeks ahead, relative to climatological or deterministic forecasts.</p>

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

2019 ◽  
Vol 20 (7) ◽  
pp. 1307-1337 ◽  
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.

scholarly journals A Comparative Verification of Localized Aviation Model Output Statistics Program (LAMP) and Numerical Weather Prediction (NWP) Model Forecasts of Ceiling Height and Visibility

2010 ◽  
Vol 25 (4) ◽  
pp. 1161-1178 ◽  
Author(s):  
David E. Rudack ◽  
Judy E. Ghirardelli
Keyword(s):  
United States ◽  
Short Range ◽  
Mesoscale Model ◽  
Weather Prediction ◽  
Weather Research And Forecasting ◽  
Model Output ◽  
Probabilistic Forecasts ◽  
Ceiling Height ◽  
Nwp Model ◽  
Model Output Statistics

Abstract In an effort to support aviation forecasting, the National Weather Service’s Meteorological Development Laboratory (MDL) has recently redeveloped the Localized Aviation Model Output Statistics (MOS) Program (LAMP) system. LAMP is designed to run hourly in NWS operations and produce short-range aviation forecast guidance at 1-h projections out to 25 h. This paper compares and contrasts LAMP ceiling height and visibility forecasts with forecasts produced by the 20-km Rapid Update Cycle model (RUC20), the Weather Research and Forecasting Nonhydrostatic Mesoscale Model (WRF-NMM), and the Short-Range Ensemble Forecast system (SREF). RUC20 and WRF-NMM forecasts of continuous ceiling height and visibility were interpolated to stations and converted into categorical forecasts. These interpolated forecasts were also categorized into instrument flight rule (IFR) or lower conditions and verified against LAMP forecasts at stations in the contiguous United States. LAMP and SREF probabilistic forecasts of ceiling height and visibility from LAMP and the SREF system were also verified. This study demonstrates that for the 0000 and 1200 UTC cycles over the contiguous United States, LAMP station-based categorical forecasts of ceiling height, visibility, and IFR conditions or lower are more accurate than the RUC20 and WRF-NMM ceiling height and visibility forecasts interpolated to stations. Moreover, for the 0900 and 2100 UTC forecast cycles and verification periods studied here, LAMP ceiling height and visibility probabilities exhibit better reliability and skill than the SREF system.

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