Investigating the sensitivity of hurricane intensity and trajectory to sea surface temperatures using the regional model WRF

The influence of sea surface temperature (SST) anomalies on the hurricane characteristics are investigated in a set of sensitivity experiments employing the Weather Research and Forecasting (WRF) model. The idealised experiments are performed for the case of Hurricane Katrina in 2005. The first set of sensitivity experiments with basin-wide changes of the SST magnitude shows that the intensity goes along with changes in the SST, i.e., an increase in SST leads to an intensification of Katrina. Additionally, the trajectory is shifted to the west (east), with increasing (decreasing) SSTs. The main reason is a strengthening of the background flow. The second set of experiments investigates the influence of Loop Current eddies idealised by localised SST anomalies. The intensity of Hurricane Katrina is enhanced with increasing SSTs close to the core of a tropical cyclone. Negative nearby SST anomalies reduce the intensity. The trajectory only changes if positive SST anomalies are located west or north of the hurricane centre. In this case the hurricane is attracted by the SST anomaly which causes an additional moisture source and increased vertical winds.

Sensitivity of the WRF model to PBL parametrisations and nesting techniques: evaluation of wind storms over complex terrain

Simulating surface wind over complex terrain is a challenge in regional climate modelling. Therefore, this study aims at identifying a set-up of the Weather Research and Forecasting Model (WRF) model that minimises system- atic errors of surface winds in hindcast simulations. Major factors of the model configuration are tested to find a suitable set-up: the horizontal resolution, the planetary boundary layer (PBL) parameterisation scheme and the way the WRF is nested to the driving data set. Hence, a number of sensitivity simulations at a spatial resolution of 2 km are carried out and compared to observations. Given the importance of wind storms, the analysis is based on case studies of 24 historical wind storms that caused great economic damage in Switzerland. Each of these events is downscaled using eight different model set-ups, but sharing the same driving data set. The results show that the lack of representation of the unresolved topography leads to a general overestimation of wind speed in WRF. However, this bias can be substantially reduced by using a PBL scheme that explicitly considers the effects of non-resolved topography, which also improves the spatial structure of wind speed over Switzerland. The wind direction, although generally well reproduced, is not very sensitive to the PBL scheme. Further sensitivity tests include four types of nesting methods: nesting only at the boundaries of the outermost domain, analysis nudging, spectral nudging, and the so-called re-forecast method, where the simulation is frequently restarted. These simulations show that restricting the freedom of the model to develop large-scale disturbances slightly increases the temporal agreement with the observations, at the same time that it further reduces the overestimation of wind speed, especially for maximum wind peaks. The model performance is also evaluated in the outermost domains, where the resolution is coarser. The results demonstrate the important role of horizontal resolution, where the step from 6 to 2 km significantly improves model performance. In summary, the combination of a grid size of 2 km, the non-local PBL scheme modified to explicitly account for non-resolved orography, as well as analysis or spectral nudging, is a superior combination when dynamical downscaling is aimed at reproducing real wind fields.

Atmospheric water parameters measured by a ground-based microwave radiometer and compared with the WRF model

The microwave radiometer TROWARA measures integrated water vapour (IWV) and integrated cloud liquid water (ILW) at Bern since 1994 with a time resolution of 7 s. In this study, we compare TROWARA measurements with a simulation of summer 2012 in Switzerland performed with the Weather Research and Forecasting (WRF) model. It is found that the WRF model agrees very well with TROWARA’s IWV variations with a mean bias of only 0.7 mm. The ILW distribution of the WRF model, although similar in shape to TROWARA’s distribution, overestimates the fraction of clear sky periods (83% compared to 60%).

Influence of microphysical schemes on atmospheric water in the Weather Research and Forecasting model

This study examines how different microphysical parameterization schemes influence orographically induced precipitation and the distributions of hydrometeors and water vapour for midlatitude summer conditions in the Weather Research and Forecasting (WRF) model. A high-resolution two-dimensional idealized simulation is used to assess the differences between the schemes in which a moist air flow is interacting with a bell-shaped 2 km high mountain. Periodic lateral boundary conditions are chosen to recirculate atmospheric water in the domain. It is found that the 13 selected microphysical schemes conserve the water in the model domain. The gain or loss of water is less than 0.81% over a simulation time interval of 61 days. The differences of the microphysical schemes in terms of the distributions of water vapour, hydrometeors and accumulated precipitation are presented and discussed. The Kessler scheme, the only scheme without ice-phase processes, shows final values of cloud liquid water 14 times greater than the other schemes. The differences among the other schemes are not as extreme, but still they differ up to 79% in water vapour, up to 10 times in hydrometeors and up to 64% in accumulated precipitation at the end of the simulation. The microphysical schemes also differ in the surface evaporation rate. The WRF single-moment 3-class scheme has the highest surface evaporation rate compensated by the highest precipitation rate. The different distributions of hydrometeors and water vapour of the microphysical schemes induce differences up to 49 W m−2 in the downwelling shortwave radiation and up to 33 W m−2 in the downwelling longwave radiation.

Modeling the formation and aging of secondary organic aerosols during CalNex 2010

Four different literature parameterizations for the formation and evolution of urban secondary organic aerosol (SOA) frequently used in 3-D models are evaluated using a 0-D box model representing the Los Angeles metropolitan region during the California Research at the Nexus of Air Quality and Climate Change (CalNex) 2010 campaign. We constrain the model predictions with measurements from several platforms and compare predictions with particle- and gas-phase observations from the CalNex Pasadena ground site. That site provides a unique opportunity to study aerosol formation close to anthropogenic emission sources with limited recirculation. The model SOA that formed only from the oxidation of VOCs (V-SOA) is insufficient to explain the observed SOA concentrations, even when using SOA parameterizations with multi-generation oxidation that produce much higher yields than have been observed in chamber experiments, or when increasing yields to their upper limit estimates accounting for recently reported losses of vapors to chamber walls. The Community Multiscale Air Quality (WRF-CMAQ) model (version 5.0.1) provides excellent predictions of secondary inorganic particle species but underestimates the observed SOA mass by a factor of 25 when an older VOC-only parameterization is used, which is consistent with many previous model–measurement comparisons for pre-2007 anthropogenic SOA modules in urban areas. Including SOA from primary semi-volatile and intermediate-volatility organic compounds (P-S/IVOCs) following the parameterizations of Robinson et al. (2007), Grieshop et al. (2009), or Pye and Seinfeld (2010) improves model–measurement agreement for mass concentration. The results from the three parameterizations show large differences (e.g., a factor of 3 in SOA mass) and are not well constrained, underscoring the current uncertainties in this area. Our results strongly suggest that other precursors besides VOCs, such as P-S/IVOCs, are needed to explain the observed SOA concentrations in Pasadena. All the recent parameterizations overpredict urban SOA formation at long photochemical ages (3 days) compared to observations from multiple sites, which can lead to problems in regional and especially global modeling. However, reducing IVOC emissions by one-half in the model to better match recent IVOC measurements improves SOA predictions at these long photochemical ages. Among the explicitly modeled VOCs, the precursor compounds that contribute the greatest SOA mass are methylbenzenes. Measured polycyclic aromatic hydrocarbons (naphthalenes) contribute 0.7% of the modeled SOA mass. The amounts of SOA mass from diesel vehicles, gasoline vehicles, and cooking emissions are estimated to be 16–27, 35–61, and 19–35 %, respectively, depending on the parameterization used, which is consistent with the observed fossil fraction of urban SOA, 71(+-3) %. The relative contribution of each source is uncertain by almost a factor of 2 depending on the parameterization used. In-basin biogenic VOCs are predicted to contribute only a few percent to SOA. A regional SOA background of approximately 2.1 μgm-3 is also present due to the long-distance transport of highly aged OA, likely with a substantial contribution from regional biogenic SOA. The percentage of SOA from diesel vehicle emissions is the same, within the estimated uncertainty, as reported in previous work that analyzed the weekly cycles in OA concentrations (Bahreini et al., 2012; Hayes et al., 2013). However, the modeling work presented here suggests a strong anthropogenic source of modern carbon in SOA, due to cooking emissions, which was not accounted for in those previous studies and which is higher on weekends. Lastly, this work adapts a simple two-parameter model to predict SOA concentration and O/C from urban emissions. This model successfully predicts SOA concentration, and the optimal parameter combination is very similar to that found for Mexico City. This approach provides a computationally inexpensive method for predicting urban SOA in global and climate models. We estimate pollution SOA to account for 26 Tg yr-1 of SOA globally, or 17% of global SOA, one third of which is likely to be non-fossil.

M-AARE - Coupling atmospheric, hydrological, hydrodynamic and damage models in the Aare river basin, Switzerland

The triggering mechanism and the temporal evolution of large flood events, especially of worst-case scenarios, are not yet fully understood. Consequently, the cumulative losses of extreme floods are unknown. To study the link between weather conditions, discharges and flood losses it is necessary to couple atmospheric, hydrological, hydrodynamic and damage models. The objective of the M-AARE project is to test the potentials and opportunities of a model chain that relates atmospheric conditions to flood losses or risks. The M-AARE model chain is a set of coupled models consisting of four main components: the precipitation module, the hydrology module, the hydrodynamic module, and the damage module. The models are coupled in a cascading framework with harmonized time-steps. First exploratory applications show that the one way coupling of the WRF-PREVAH-BASEMENT models has been achieved and provides promising new insights for a better understanding of key aspects in flood risk analysis.