scholarly journals Adjoint Sensitivity of North Pacific Atmospheric River Forecasts

2019 ◽  
Vol 147 (6) ◽  
pp. 1871-1897 ◽  
Author(s):  
Carolyn A. Reynolds ◽  
James D. Doyle ◽  
F. Martin Ralph ◽  
Reuben Demirdjian
Keyword(s):  
Kinetic Energy ◽  
Forecast Error ◽  
Meridional Wind ◽  
Precipitation Forecast ◽  
Initial State ◽  
Adjoint Sensitivity ◽  
Optimal Perturbation ◽  
Low Level ◽  
Atmospheric River ◽  
Perturbation Growth

Abstract The initial-state sensitivity and optimal perturbation growth for 24- and 36-h forecasts of low-level kinetic energy and precipitation over California during a series of atmospheric river (AR) events that took place in early 2017 are explored using adjoint-based tools from the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS). This time period was part of the record-breaking winter of 2016–17 in which several high-impact ARs made landfall in California. The adjoint sensitivity indicates that both low-level winds and precipitation are most sensitive to mid- to lower-tropospheric perturbations in the initial state in and near the ARs. A case study indicates that the optimal moist perturbations occur most typically along the subsaturated edges of the ARs, in a warm conveyor belt region. The sensitivity to moisture is largest, followed by temperature and winds. A 1 g kg−1 perturbation to moisture may elicit twice as large a response in kinetic energy and precipitation as a 1 m s−1 perturbation to the zonal or meridional wind. In an average sense, the sensitivity and related optimal perturbations are very similar for the kinetic energy and precipitation response functions. However, on a case-by-case basis, differences in the sensitivity magnitude and optimal perturbation structures result in substantially different forecast perturbations, suggesting that optimal adaptive observing strategies should be metric dependent. While the nonlinear evolved perturbations are usually smaller (by about 20%, on average) than the expected linear perturbations, the optimal perturbations are still capable of producing rapid nonlinear perturbation growth. The positive correlation between sensitivity magnitude and wind speed forecast error or precipitation forecast differences supports the relevance of adjoint-based calculations for predictability studies.

scholarly journals A Case Study of the Physical Processes Associated with the Atmospheric River Initial-Condition Sensitivity from an Adjoint Model

2020 ◽  
Vol 77 (2) ◽  
pp. 691-709 ◽  
Author(s):  
Reuben Demirdjian ◽  
James D. Doyle ◽  
Carolyn A. Reynolds ◽  
Joel R. Norris ◽  
Allison C. Michaelis ◽  
...  
Keyword(s):  
Precipitation Forecast ◽  
Water Vapor Transport ◽  
Orographic Precipitation ◽  
Initial Condition ◽  
Latent Heating ◽  
Dynamic Component ◽  
Low Level ◽  
Atmospheric River ◽  
Transverse Circulation

Abstract Analysis of a strong landfalling atmospheric river is presented that compares the evolution of a control simulation with that of an adjoint-derived perturbed simulation using the Coupled Ocean–Atmosphere Mesoscale Prediction System. The initial-condition sensitivities are optimized for all state variables to maximize the accumulated precipitation within the majority of California. The water vapor transport is found to be substantially enhanced at the California coast in the perturbed simulation during the time of peak precipitation, demonstrating a strengthened role of the orographic precipitation forcing. Similarly, moisture convergence and vertical velocities derived from the transverse circulation are found to be substantially enhanced during the time of peak precipitation, also demonstrating a strengthened role of the dynamic component of the precipitation. Importantly, both components of precipitation are associated with enhanced latent heating by which (i) a stronger diabatically driven low-level potential vorticity anomaly strengthens the low-level wind (and thereby the orographic precipitation forcing), and (ii) greater moist diabatic forcing enhances the Sawyer–Eliassen transverse circulation and thereby increases ascent and dynamic precipitation. A Lagrangian parcel trajectory analysis demonstrates that a positive moisture perturbation within the atmospheric river increases the moisture transport into the warm conveyor belt offshore, which enhances latent heating in the perturbed simulation. These results suggest that the precipitation forecast in this case is particularly sensitive to the initial moisture content within the atmospheric river due to its role in enhancing both the orographic precipitation forcing and the dynamic component of precipitation.

scholarly journals Adjoint Sensitivity Analysis of High-Impact Extratropical Cyclones

2019 ◽  
Vol 147 (12) ◽  
pp. 4511-4532 ◽  
Author(s):  
James D. Doyle ◽  
Carolyn A. Reynolds ◽  
Clark Amerault
Keyword(s):  
Kinetic Energy ◽  
Potential Vorticity ◽  
Linear Models ◽  
High Impact ◽  
Initial Time ◽  
Extratropical Cyclones ◽  
Precipitation Kinetic ◽  
Initial State ◽  
Adjoint Sensitivity ◽  
The North

Abstract The initial state sensitivity of high-impact extratropical cyclones over the North Atlantic and United Kingdom is investigated using an adjoint modeling system that includes moist processes. The adjoint analysis indicates that the 48-h forecast of precipitation and high winds associated with the extratropical cyclone “Desmond” was highly sensitive to mesoscale regions of moisture at the initial time. Mesoscale moisture and potential vorticity structures along the poleward edge of an atmospheric river at the initialization time had a large impact on the development of Desmond as demonstrated with precipitation, kinetic energy, and potential vorticity response functions. Adjoint-based optimal perturbations introduced into the initial state exhibit rapidly growing amplitudes through moist energetic processes over the 48-h forecast. The sensitivity manifests as an upshear-tilted structure positioned along the cold and warm fronts. Perturbations introduced into the nonlinear and tangent linear models quickly expand vertically and interact with potential vorticity anomalies in the mid- and upper levels. Analysis of adjoint sensitivity results for the winter 2013/14 show that the moisture sensitivity magnitude at the initial time is well correlated with the kinetic energy error at the 36-h forecast time, which supports the physical significance and importance of the mesoscale regions of high moisture sensitivities.

scholarly journals Adjoint Sensitivity and Predictability of Tropical Cyclogenesis

2012 ◽  
Vol 69 (12) ◽  
pp. 3535-3557 ◽  
Author(s):  
James D. Doyle ◽  
Carolyn A. Reynolds ◽  
Clark Amerault ◽  
Jonathan Moskaitis
Keyword(s):  
Linear Models ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Temperature Fields ◽  
Tropical Cyclogenesis ◽  
Wind Fields ◽  
Initial State ◽  
Adjoint Sensitivity ◽  
Optimal Perturbation ◽  
Sensitivity Maximum

Abstract The sensitivity of tropical cyclogenesis and subsequent intensification is explored by applying small perturbations to the initial state in the presence of organized mesoscale convection and synoptic-scale forcing using the adjoint and tangent linear models for the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS). The forward, adjoint, and tangent linear models are used to compare and contrast predictability characteristics for the disturbance that became Typhoon Nuri and a nondeveloping organized convective cluster in the western Pacific during The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (T-PARC) and the Tropical Cyclone Structure-2008 (TCS-08) experiments. The adjoint diagnostics indicate that the intensity (e.g., maximum surface wind speed, minimum surface pressure) of a tropical disturbance is very sensitive to perturbations in the moisture and temperature fields and to a lesser degree the wind fields. The highest-resolution adjoint simulations (grid increment of 13 km) indicate that the most efficient intensification is through moistening in the lower and middle levels and heating in banded regions that are coincident with vorticity maxima in the initial state. Optimal adjoint perturbations exhibit rapid growth for the Nuri case and only modest growth for the nondeveloping system. The adjoint results suggest that Nuri was near the threshold for development, indicative of low predictability. The low-level sensitivity maximum and tendency for optimal perturbation growth to extend vertically through the troposphere are consistent with a “bottom up” development process of TC genesis, although a secondary midlevel sensitivity maximum is present as well. Growth originates at small scales and projects onto the scale of the vortex, a manifestation of perturbations that project onto organized convection embedded in regions of cyclonic vorticity.

scholarly journals Budgets of rotational and divergent kinetic energy in the warm-sector torrential rains over South China: a case study

2021 ◽  
Author(s):  
Shui-Xin Zhong ◽  
Wei-Guang Meng ◽  
Fu-You Tian
Keyword(s):  
Kinetic Energy ◽  
Energy Budget ◽  
South China ◽  
Extreme Precipitation ◽  
Flux Divergence ◽  
Low Level ◽  
Warm Sector ◽  
Kinetic Energy Budget ◽  
Rotational Components ◽  
The Pearl River

AbstractThe contributions of divergent and rotational wind components to the kinetic energy budget during a record-breaking rainstorm on 7 May 2017 over South China are examined. This warm-sector extreme precipitation caused historical maximum of 382.6 mm accumulated rainfall in 3 h over the Pearl River Delta (PRD) regions in South China. Results show that there was a high low-level southerly wind-speed tongue stretching into the PRD regions from the northeast of the South China Sea (SCS) during this extreme precipitation. The velocity potential exhibited a low-value center as well as a low-level divergence-center over the SCS. The rotational components of the kinetic energy (KR)-related terms were the main contribution-terms of the kinetic energy budget. The main contribution-terms of KR and the divergent component of kinetic energy (KD) were the barotropical and baroclinic processes-related terms due to cross-contour flow and the vertical flux divergence.

scholarly journals Multiscale Mountain Waves Influencing a Major Orographic Precipitation Event

2007 ◽  
Vol 64 (3) ◽  
pp. 711-737 ◽  
Author(s):  
Matthew F. Garvert ◽  
Bradley Smull ◽  
Cliff Mass
Keyword(s):  
Mesoscale Model ◽  
Precipitation Forecast ◽  
Precipitation Event ◽  
Small Scale ◽  
Second Phase ◽  
Mountain Waves ◽  
Low Level ◽  
Orographic Rainfall ◽  
Vertically Propagating ◽  
Horizontal Wavelength

Abstract This study combines high-resolution mesoscale model simulations and comprehensive airborne Doppler radar observations to identify kinematic structures influencing the production and mesoscale distribution of precipitation and microphysical processes during a period of heavy prefrontal orographic rainfall over the Cascade Mountains of Oregon on 13–14 December 2001 during the second phase of the Improvement of Microphysical Parameterization through Observational Verification Experiment (IMPROVE-2) field program. Airborne-based radar detection of precipitation from well upstream of the Cascades to the lee allows a depiction of terrain-induced wave motions in unprecedented detail. Two distinct scales of mesoscale wave–like air motions are identified: 1) a vertically propagating mountain wave anchored to the Cascade crest associated with strong midlevel zonal (i.e., cross barrier) flow, and 2) smaller-scale (<20-km horizontal wavelength) undulations over the windward foothills triggered by interaction of the low-level along-barrier flow with multiple ridge–valley corrugations oriented perpendicular to the Cascade crest. These undulations modulate cloud liquid water (CLW) and snow mixing ratios in the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5), with modeled structures comparing favorably to radar-documented zones of enhanced reflectivity and CLW measured by the NOAA P3 aircraft. Errors in the model representation of a low-level shear layer and the vertically propagating mountain waves are analyzed through a variety of sensitivity tests, which indicated that the mountain wave’s amplitude and placement are extremely sensitive to the planetary boundary layer (PBL) parameterization being employed. The effects of 1) using unsmoothed versus smoothed terrain and 2) the removal of upstream coastal terrain on the flow and precipitation over the Cascades are evaluated through a series of sensitivity experiments. Inclusion of unsmoothed terrain resulted in net surface precipitation increases of ∼4%–14% over the windward slopes relative to the smoothed-terrain simulation. Small-scale waves (<20-km horizontal wavelength) over the windward slopes significantly impact the horizontal pattern of precipitation and hence quantitative precipitation forecast (QPF) accuracy.

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 Optimal perturbation growth in axisymmetric intrusions

2016 ◽  
Vol 811 ◽  
Author(s):  
Bruce R. Sutherland ◽  
C. P. Caulfield
Keyword(s):  
Gravity Current ◽  
Optimal Growth ◽  
Base Flow ◽  
Optimal Perturbation ◽  
Target Time ◽  
Experimental Parameters ◽  
Radial Spokes ◽  
Perturbation Energy ◽  
The One ◽  
Perturbation Growth

The cylindrical lock-release laboratory experiments of Sutherland & Nault (J. Fluid Mech., vol. 586, 2007, pp. 109–118) showed that a radially advancing symmetric intrusive gravity current spreads not as an expanding annulus (as is the case for bottom-propagating gravity currents), but rather predominantly along azimuthally periodic radial ‘spokes’. Here, we investigate whether the spokes are associated with azimuthal perturbations that undergo ‘optimal’ growth. We use a nonlinear axisymmetric numerical simulation initialised with the experimental parameters to compute the time-evolving axisymmetric base state of the collapsing lock fluid. Using fields from this rapidly evolving base state together with the linearised perturbation equations and their adjoint, the ‘direct–adjoint looping’ method is employed to identify, as a function of the azimuthal wavenumber $m$, the vertical–radial structure of the set of initial perturbations that exhibit the largest total perturbation energy gain over a target time $T$. Of this set of perturbations, the one that extracts energy fastest, and so is expected to be observed to emerge first from the base flow, has azimuthal wavenumber comparable to the number of spokes observed in the experiment.

OPTIMAL PERTURBATION OF UNCERTAIN SYSTEMS

2002 ◽  
Vol 02 (03) ◽  
pp. 395-402 ◽  
Author(s):  
BRIAN F. FARRELL ◽  
PETROS J. IOANNOU
Keyword(s):  
Dynamical System ◽  
Uncertain Systems ◽  
Initial Condition ◽  
Statistical Characterization ◽  
Optimal Perturbation ◽  
Physical Systems ◽  
Perturbation Problem ◽  
Ensemble Mean ◽  
Perturbation Growth

In studies of perturbation dynamics in physical systems, certain specification of the governing perturbation dynamical system is generally lacking, either because the perturbation system is imperfectly known or because its specification is intrinsically uncertain, while a statistical characterization of the perturbation dynamical system is often available. In this report exact and asymptotically valid equations are derived for the ensemble mean and moment dynamics of uncertain systems. These results are used to extend the concept of optimal deterministic perturbation of certain systems to uncertain systems. Remarkably, the optimal perturbation problem has a simple solution: In uncertain systems there is a sure initial condition producing the greatest expected second moment perturbation growth.

scholarly journals Comparing Adjoint- and Ensemble-Sensitivity Analysis with Applications to Observation Targeting

2007 ◽  
Vol 135 (12) ◽  
pp. 4117-4134 ◽  
Author(s):  
Brian Ancell ◽  
Gregory J. Hakim
Keyword(s):  
Variance Reduction ◽  
Initial Conditions ◽  
Sampling Error ◽  
Forecast Error ◽  
Single Point ◽  
Error Variance ◽  
Initial Time ◽  
Forecast Errors ◽  
Adjoint Sensitivity ◽  
Time Optimal

Abstract The sensitivity of numerical weather forecasts to small changes in initial conditions is estimated using ensemble samples of analysis and forecast errors. Ensemble sensitivity is defined here by linear regression of analysis errors onto a given forecast metric. It is shown that ensemble sensitivity is proportional to the projection of the analysis-error covariance onto the adjoint-sensitivity field. Furthermore, the ensemble-sensitivity approach proposed here involves a small calculation that is easy to implement. Ensemble- and adjoint-based sensitivity fields are compared for a representative wintertime flow pattern near the west coast of North America for a 90-member ensemble of independent initial conditions derived from an ensemble Kalman filter. The forecast metric is taken for simplicity to be the 24-h forecast of sea level pressure at a single point in western Washington State. Results show that adjoint and ensemble sensitivities are very different in terms of location, scale, and magnitude. Adjoint-sensitivity fields reveal mesoscale lower-tropospheric structures that tilt strongly upshear, whereas ensemble-sensitivity fields emphasize synoptic-scale features that tilt modestly throughout the troposphere and are associated with significant weather features at the initial time. Optimal locations for targeting can easily be determined from ensemble sensitivity, and results indicate that the primary targeting locations are located away from regions of greatest adjoint and ensemble sensitivity. It is shown that this method of targeting is similar to previous ensemble-based methods that estimate forecast-error variance reduction, but easily allows for the application of statistical confidence measures to deal with sampling error.

scholarly journals A Study on the Reduction of Forecast Error Variance by Three Adaptive Observation Approaches for Tropical Cyclone Prediction

2011 ◽  
Vol 139 (7) ◽  
pp. 2218-2232 ◽  
Author(s):  
Xiaohao Qin ◽  
Mu Mu
Keyword(s):  
Tropical Cyclone ◽  
Large Scale ◽  
Forecast Error ◽  
Error Variance ◽  
Northwest Pacific ◽  
Optimal Perturbation ◽  
Observational Network ◽  
Steering Flow ◽  
Forecast Error Variance ◽  
Cyclone Prediction

Abstract Three adaptive approaches for tropical cyclone prediction are compared in this study: the conditional nonlinear optimal perturbation (CNOP) method, the first singular vector (FSV) method, and the ensemble transform Kalman filter (ETKF) method. These approaches are compared for 36-h forecasts of three northwest Pacific tropical cyclones (TCs): Matsa (2005), Nock-Ten (2004), and Morakot (2009). The sensitive regions identified by each method are obtained. The CNOPs form an annulus around the storm at the targeting time, the FSV targets areas north of the storm, and the ETKF closely targets the typhoon location itself. The sensitive results of both the CNOPs and FSV collocate well with the steering flow between the subtropical high and the TCs. Furthermore, the regions where the convection is strong are targeted by the CNOPs. Relatively speaking, the ETKF sensitive results reflect the large-scale flow. To identify the most effective adaptive observational network, numerous probes or flights were tested arbitrarily for the ETKF method or according to the calculated sensitive regions of the CNOP and FSV methods. The results show that the sensitive regions identified by these three methods are more effective for adaptive observations than the other regions. In all three cases, the optimal adaptive observational network identified by the CNOP and ETKF methods results in similar forecast improvements in the verification region at the verification time, while the improvement using the FSV method is minor.

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