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Task-oriented dialogue research has mainly focused on a few popular languages like English and Chinese, due to the high dataset creation cost for a new language. To reduce the cost, we apply manual editing to automatically translated data. We create a new multilingual benchmark, X-RiSAWOZ, by translating the Chinese RiSAWOZ to 4 languages: English, French, Hindi, Korean; and a code-mixed English- Hindi language. X-RiSAWOZ has more than 18,000 human-verified dialogue utterances for each language, and unlike most multilingual prior work, is an end-to-end dataset for building fully-functioning agents. The many difficulties we encountered in creating X-RiSAWOZ led us to develop a toolset to accelerate the post-editing of a new language dataset after translation. This toolset improves machine translation with a hybrid entity alignment technique that combines neural with dictionary-based methods, along with many automated and semi-automated validation checks. We establish strong baselines for X-RiSAWOZ by training dialogue agents in the zero- and few-shot settings where limited gold data is available in the target language. Our results suggest that our translation and post-editing methodology and toolset can be used to create new high-quality multilingual dialogue agents cost-effectively. Our dataset, 

Increased wildfire events constitute a significant threat to life and property in the United States. Wildfire impact on severe storms and weather hazards is another pathway that threatens society, and our understanding of which is very limited. Here, we use unique modeling developments to explore the effects of wildfires in the western US (mainly California and Oregon) on precipitation and hail in the central US. We find that the western US wildfires notably increase the occurrences of heavy precipitation rates by 38% and significant severe hail (≥2 in.) by 34% in the central United States. Both heat and aerosols from wildfires play an important role. By enhancing surface high pressure and increasing westerly and southwesterly winds, wildfires in the western United States produce ( 1 ) stronger moisture and aerosol transport to the central United States and ( 2 ) larger wind shear and storm-relative helicity in the central United States. Both the meteorological environment more conducive to severe convective storms and increased aerosols contribute to the enhancements of heavy precipitation rates and large hail. Moreover, the local wildfires in the central US also enhance the severity of storms, but their impact is notably smaller than the impact of remote wildfires in California and Oregon because of the lessened severity of the local wildfires. As wildfires are projected to be more frequent and severe in a warmer climate, the influence of wildfires on severe weather in downwind regions may become increasingly important. 

Abstract. New particle formation (NPF) and subsequent particle growth are importantsources of condensation nuclei (CN) and cloud condensation nuclei (CCN).While many observations have shown positive contributions of NPF to CCN atlow supersaturation, negative NPF contributions were often simulated inpolluted environments. Using the observations in a coastal city of Qingdao,Beijing, and Gucheng in north China, we thoroughly evaluate the simulatednumber concentrations of CN and CCN using an NPF-explicit parameterizationembedded in the WRF-Chem model. For CN, the initial simulation shows largebiases of particle number concentrations at 10–40 and 40–100 nm. Byadjusting the process of gas–particle partitioning, including the massaccommodation coefficient (MAC) of sulfuric acid, the phase changes in primary organic aerosol emissions, and the condensational amount of nitric acid, the improvement of the particle growth process yields substantially reduced overestimation of CN. Regarding CCN, secondary organic aerosol (SOA) formed from the oxidation of semi-volatile and intermediate-volatility organic compounds (S/IVOCs) is called SI-SOA, the yield of which is an important contributor. At default settings, the SI-SOA yield is too high without considering the differences in precursor oxidation rates. Lowering the SI-SOA yield under linear H2SO4 nucleation scheme results in much-improved CCN simulations compared to observations. On the basis of the bias-corrected model, we find substantially positive contributions of NPF to CCN at low supersaturation (∼ 0.2 %) over broad areas of China, primarily due to competing effects of increasing particle hygroscopicity, a result of reductions in SI-SOA amount, surpassing that of particle size decreases. The bias-corrected model is robustly applicable to other schemes, such as the quadratic H2SO4 nucleation scheme, in terms of CN and CCN, though the dependence of CCN on SI-SOA yield is diminished likely due to changes in particle composition. This study highlights potentially much larger NPF contributions to CCN on a regional and even global basis.

 

Abstract. The GoAmazon 2014/5 field campaign took place in Manaus, Brazil, and allowed the investigation of the interaction between background-level biogenic air masses and anthropogenic plumes.We present in this work a box model built to simulate the impact of urban chemistry on biogenic secondary organic aerosol (SOA) formation and composition.An organic chemistry mechanism is generated with the Generator for Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A) to simulate the explicit oxidation of biogenic and anthropogenic compounds.A parameterization is also included to account for the reactive uptake of isoprene oxidation products on aqueous particles.The biogenic emissions estimated from existing emission inventories had to be reduced to match measurements.The model is able to reproduce ozone and NOx for clean and polluted situations.The explicit model is able to reproduce background case SOA mass concentrations but does not capture the enhancement observed in the urban plume.The oxidation of biogenic compounds is the major contributor to SOA mass.A volatility basis set (VBS) parameterization applied to the same cases obtains better results than GECKO-A for predicting SOA mass in the box model.The explicit mechanism may be missing SOA-formation processes related to the oxidation of monoterpenes that could be implicitly accounted for in the VBS parameterization. 

Understanding the formation processes of particles and cloud condensation nuclei (CCN) in pristine environments is a major challenge in assessing the anthropogenic impacts on climate change. Using a state‐of‐the‐art model that systematically simulates the new‐particle formation (NPF) from condensable vapors and multi‐scale transport of chemical species, we find that NPF contributes ∼90% of the particle number and ∼80% of the CCN at 0.5% supersaturation (CCN0.5%) in the pristine Amazon boundary layer during the wet season. The corresponding contributions are only ∼30% and ∼20% during the dry season because of prevalent biomass burning. In both seasons, ∼50% of the NPF‐induced particles and ∼85% of the NPF‐induced CCN0.5% in the boundary layer originate from the long‐range transport of new particles formed hundreds to thousands of kilometers away. Moreover, about 50%–65% of the NPF‐induced particles and 35%–50% of the NPF‐induced CCN0.5% originate from the downward transport of new particles formed aloft.