On the existence of the logarithmic surface layer in the inner-core of hurricanes

Q. J. R. Meteorol. Soc., 1394128

Pulse‐labeling studies of carbon cycling in arctic tundra ecosystems: Contribution of photosynthates to soil organic matter

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95486/1/gbc827.pd

Recommendations for improved tropical cyclone formation and position probabilistic Forecast products

Prediction of the potentially devastating impact of landfalling tropical cyclones (TCs) relies substantially on numerical prediction systems. Due to the limited predictability of TCs and the need to express forecast confidence and possible scenarios, it is vital to exploit the benefits of dynamic ensemble forecasts in operational TC forecasts and warnings. RSMCs, TCWCs, and other forecast centers value probabilistic guidance for TCs, but the International Workshop on Tropical Cyclones (IWTC-9) found that the “pull-through” of probabilistic information to operational warnings using those forecasts is slow. IWTC-9 recommendations led to the formation of the WMO/WWRP Tropical Cyclone-Probabilistic Forecast Products (TC-PFP) project, which is also endorsed as a WMO Seamless GDPFS Pilot Project. The main goal of TC-PFP is to coordinate across forecast centers to help identify best practice guidance for probabilistic TC forecasts. TC-PFP is being implemented in 3 phases: Phase 1 (TC formation and position); Phase 2 (TC intensity and structure); and Phase 3 (TC related rainfall and storm surge). This article provides a summary of Phase 1 and reviews the current state of the science of probabilistic forecasting of TC formation and position. There is considerable variability in the nature and interpretation of forecast products based on ensemble information, making it challenging to transfer knowledge of best practices across forecast centers. Communication among forecast centers regarding the effectiveness of different approaches would be helpful for conveying best practices. Close collaboration with experts experienced in communicating complex probabilistic TC information and sharing of best practices between centers would help to ensure effective decisions can be made based on TC forecasts. Finally, forecast centers need timely access to ensemble information that has consistent, user-friendly ensemble information. Greater consistency across forecast centers in data accessibility, probabilistic forecast products, and warnings and their communication to users will produce more reliable information and support improved outcomes

A model-data comparison of gross primary productivity: Results from the North American Carbon Program site synthesis

Accurately simulating gross primary productivity (GPP) in terrestrial ecosystem models is critical because errors in simulated GPP propagate through the model to introduce additional errors in simulated biomass and other fluxes. We evaluated simulated, daily average GPP from 26 models against estimated GPP at 39 eddy covariance flux tower sites across the United States and Canada. None of the models in this study match estimated GPP within observed uncertainty. On average, models overestimate GPP in winter, spring, and fall, and underestimate GPP in summer. Models overpredicted GPP under dry conditions and for temperatures below 0°C. Improvements in simulated soil moisture and ecosystem response to drought or humidity stress will improve simulated GPP under dry conditions. Adding a low-temperature response to shut down GPP for temperatures below 0°C will reduce the positive bias in winter, spring, and fall and improve simulated phenology. The negative bias in summer and poor overall performance resulted from mismatches between simulated and observed light use efficiency (LUE). Improving simulated GPP requires better leaf-to-canopy scaling and better values of model parameters that control the maximum potential GPP, such as ε[subscript max] (LUE), V[subscript cmax] (unstressed Rubisco catalytic capacity) or J[subscript max] (the maximum electron transport rate)

Using ensemble data assimilation for predictability and dynamics

Thesis (Ph. D.)--University of Washington, 2007.Atmospheric predictability depends in part on the sources and evolution of errors in numerical weather prediction models. As a consequence, it is important to initialize a model with the best estimate of the atmosphere and understand how errors in this initial condition will affect the forecast. The ensemble Kalman filter (EnKF) is an attractive method of initializing a forecast model because this technique uses statedependent error statistics to spread observation information to model grid points. In addition, output from an EnKF system can be used to quantify how changes to the initial conditions and observation assimilation affect scalar functions of forecast variables.A pseudo-operational EnKF system is implemented for a limited-area domain that includes the eastern Pacific Ocean to test the benefit of ensemble analyses and forecasts in a region characterized by sparse in-situ data and complex topography. Comparisons against rawinsondes indicate that ensemble forecasts from this system have comparable skill to other major global NWP forecasts, even though it does not consider satellite radiance data. Forecasts of average pressure over western Washington state from this EnKF system show a region of maximum sensitivity to the west of this region. The accuracy of ensemble predictions of observation impact is verified by comparing forecasts where observations are assimilated with the control case where no observations are used. These experiments indicate that the impact of thousands of observations can be estimated by a subset of O(100) most-significant observations.These ensemble techniques are applied to understand the initial condition sensitivity and observation impact during forecasts of western Pacific extratropical transition (ET) events, which are often characterized by large short-term forecast er rors. ET forecasts are most sensitive to the position of the tropical cyclone (TC) and to upstream mid-latitude troughs that interact with the transitioning storm and other downstream features. Observation impact calculations indicate that assimilating O(50) observations near the TC and upstream troughs can have nearly the same impact as all 12 000 available observations. Furthermore, the amount of downstream ridging that occurs during these events depends on the lower-tropospheric moisture flux east of the TC

Validation of Ensemble-Based Probabilistic Tropical Cyclone Intensity Change

Although there has been substantial improvement to numerical weather prediction models, accurate predictions of tropical cyclone rapid intensification (RI) remain elusive. The processes that govern RI, such as convection, may be inherently less predictable; therefore a probabilistic approach should be adopted. Although there have been numerous studies that have evaluated probabilistic intensity (i.e., maximum wind speed) forecasts from high resolution models, or statistical RI predictions, there has not been a comprehensive analysis of high-resolution ensemble predictions of various intensity change thresholds. Here, ensemble-based probabilities of various intensity changes are computed from experimental Hurricane Weather Research and Forecasting (HWRF) and Hurricanes in a Multi-scale Ocean-coupled Non-hydrostatic (HMON) models that were run for select cases during the 2017–2019 seasons and verified against best track data. Both the HWRF and HMON ensemble systems simulate intensity changes consistent with RI (30 knots 24 h−1; 15.4 m s−1 24 h−1) less frequent than observed, do not provide reliable probabilistic predictions, and are less skillful probabilistic forecasts relative to the Statistical Hurricane Intensity Prediction System Rapid Intensification Index (SHIPS-RII) and Deterministic to Probabilistic Statistical (DTOPS) statistical-dynamical systems. This issue is partly alleviated by applying a quantile-based bias correction scheme that preferentially adjusts the model-based intensity change at the upper-end of intensity changes. While such an approach works well for high-resolution models, this bias correction strategy does not substantially improve ECMWF ensemble-based probabilistic predictions. By contrast, both the HWRF and HMON systems provide generally reliable predictions of intensity changes for cases where RI does not take place. Combining the members from the HWRF and HMON ensemble systems into a large multi-model ensemble does not improve upon HMON probablistic forecasts

Validation of Ensemble-Based Probabilistic Tropical Cyclone Intensity Change

Although there has been substantial improvement to numerical weather prediction models, accurate predictions of tropical cyclone rapid intensification (RI) remain elusive. The processes that govern RI, such as convection, may be inherently less predictable; therefore a probabilistic approach should be adopted. Although there have been numerous studies that have evaluated probabilistic intensity (i.e., maximum wind speed) forecasts from high resolution models, or statistical RI predictions, there has not been a comprehensive analysis of high-resolution ensemble predictions of various intensity change thresholds. Here, ensemble-based probabilities of various intensity changes are computed from experimental Hurricane Weather Research and Forecasting (HWRF) and Hurricanes in a Multi-scale Ocean-coupled Non-hydrostatic (HMON) models that were run for select cases during the 2017–2019 seasons and verified against best track data. Both the HWRF and HMON ensemble systems simulate intensity changes consistent with RI (30 knots 24 h−1 role= presentation \u3e−1; 15.4 m s−1 role= presentation \u3e−1 24 h−1 role= presentation \u3e−1) less frequent than observed, do not provide reliable probabilistic predictions, and are less skillful probabilistic forecasts relative to the Statistical Hurricane Intensity Prediction System Rapid Intensification Index (SHIPS-RII) and Deterministic to Probabilistic Statistical (DTOPS) statistical-dynamical systems. This issue is partly alleviated by applying a quantile-based bias correction scheme that preferentially adjusts the model-based intensity change at the upper-end of intensity changes. While such an approach works well for high-resolution models, this bias correction strategy does not substantially improve ECMWF ensemble-based probabilistic predictions. By contrast, both the HWRF and HMON systems provide generally reliable predictions of intensity changes for cases where RI does not take place. Combining the members from the HWRF and HMON ensemble systems into a large multi-model ensemble does not improve upon HMON probablistic forecast

Probabilistic Verification of Global and Mesoscale Ensemble Forecasts of Tropical Cyclogenesis

Abstract Probabilistic forecasts of tropical cyclogenesis have been evaluated for two samples: a near-homogeneous sample of ECMWF and Weather Research and Forecasting (WRF) Model–ensemble Kalman filter (EnKF) ensemble forecasts during the National Science Foundation’s (NSF) Pre-Depression Investigation of Cloud-systems in the Tropics (PREDICT) field campaign (15 August–30 September 2010) and ECMWF ensemble forecasts during the 2010–12 Atlantic hurricane seasons. Quantitative criteria for tropical cyclone (TC) formation were first determined from model analyses based on threshold values of lower-tropospheric circulation, local thickness anomaly, and minimum sea level pressure. A binary verification was then performed for all ensemble forecasts with initial-time tropical disturbances. During the PREDICT period, the ECMWF and WRF–EnKF had similar verification statistics, with reliability diagrams of positive slope flatter than unity, and relative operating characteristic (ROC) curves that demonstrate skill. For the 2010–12 ECMWF ensemble forecasts, the equitable threat score was small and positive, with skill mostly lost after 5 days. The reliability diagrams for 1–5-day forecasts were monotonic increasing, though an overly large number of short-range ensemble forecasts predicted a low probability of a TC when a TC was verified. The ROC curves exhibited similar skill for forecasts out to 5 days. The reliability curves were sensitive to parameters such as time tolerance and threshold values, and insensitive to cases that originated from African easterly waves versus those that did not. Qualitative investigations revealed case-to-case variability in the probabilistic predictions. While the sample size was limited, the ensembles showed the potential for probabilistic prediction out to 5 days, though it appeared that the model struggled with developing a warm core in the short-range forecast

African Easterly Wave Forecast Verification and Its Relation to Convective Errors within the ECMWF Ensemble Prediction System

African easterly waves (AEWs) are the primary synoptic-scale weather feature found in sub-Saharan Africa during boreal summer, yet there have been few studies documenting the performance of operational ensemble prediction systems (EPSs) for these phenomena. Here, AEW forecasts in the 51-member ECMWF EPS are validated against an average of four operational analyses during two periods of enhanced AEW activity (July–September 2007–09 and 2011–13). During 2007–09, AEW position forecasts were mainly underdispersive and characterized by a slow bias, while intensity forecasts were characterized by an overintensification bias, yet the ensemble-mean errors generally matched the forecast uncertainty. Although 2011–13 position forecasts were still underdispersive with a slow bias, the ensemble-mean error is smaller than for 2007–09. In addition, the 2011–13 intensity forecasts were overdispersive and had a negligible intensity bias. Forecasts from 2007 to 2009 were characterized by higher precipitation in the AEW trough center and high correlations between divergence errors and intensity errors, suggesting the intensity bias is associated with errors in convection. By contrast, forecasts from 2011 to 2013 have smaller precipitation biases than those from 2007 to 2009 and exhibit a weaker correlation between divergence errors and intensity errors, suggesting a weaker connection between AEW forecast errors and convective errors