Generating Weather Comments from Meteorological Simulations (2021)

Abstract: The task of generating weather-forecast comments from meteorological simulations has the following requirements: (i) the changes in numerical values for various physical quantities need to be considered, (ii) the weather comments should be dependent on delivery time and area information, and (iii) the comments should provide useful information for users. To meet these requirements, we propose a data-to-text model that incorporates three types of encoders for numerical forecast maps, observation data, and meta-data. We also introduce weather labels representing weather information, such as sunny and rain, for our model to explicitly describe useful information. We conducted automatic and human evaluations. The results indicate that our model performed best against baselines in terms of informativeness. We make our code and data publicly available.

## introduction numerical weather prediction (nwp), a method for weather forecasting that uses mathematical models of the atmosphere, oceans, and observations, has become a mainstream tool for supporting today's weather forecasts around the world. weather forecasters obtain numerical outputs from the simulation models and use their scientific knowledge and historical data to come up with forecast comments such as "sunny and sometimes cloudy". however, writing local or personalized weather comments for end users is labor intensive and requires a solid knowledge of meteorology. therefore, the task of generating weather-forecast comments has traditionally been addressed in the field of data-to-text generation @xcite . in this paper, we focus on the task of generating weather-forecast comments from meteorological 1 https://github.com/titech-nlp/ pinpoint-weather ## related work data-to-text generation, which is the task of automatically producing descriptions from nonlinguistic data @xcite , has been widely used in various fields such as sports @xcite , finance @xcite @xcite , and medical care @xcite . neural generation methods have been attracting increased attention in the field of data-totext generation @xcite , al-though rule-based approaches have been the mainstream @xcite . the task of generating weather-forecast comments has traditionally been tackled in the field of data-to-text generation @xcite @xcite . for example, there are efforts in generating weather-forecast comments intended for marine shipping or offshore oil facilities @xcite , as well as local weather forecasts for more general use @xcite . prior research has examined the second and third problems mentioned in section 1 @xcite . for the second problem, we need to incorporate information for time and area into a generation model to generate time-dependent expressions. for the third problem, we must carry out content selection to explicitly provide useful information, such as sunny and rain, for consumers. in the table-to-text task, which aims to generate a description from a structured table, there have been recent efforts to improve the correctness of generated texts by implicitly introducing a content-matching constraint @xcite , explicitly specifying the content in the table @xcite or incorporating copy mechanism @xcite . nonetheless, the techniques proposed in the table-to-text task are not directly applicable to datasets consisting of raw numerical data, such as simulation results of nwp models, and texts since they rely on task-specific architectures such as the copy mechanism copying words from tables. in addition, @xcite proposed a method for generating summaries of basketball games by using the correspondence between entities in text and input tabular data extracted using the information-extraction method @xcite . however, the methods are also not applicable to datasets consisting of raw numerical data and texts because they rely heavily on a word-matching algorithm between input tables and texts. to overcome this limitation, we extract weather labels representing the content of weather information from only text on the basis of clue words and use them to explicitly describe the useful information. ## weather data weather forecasters obtain the output of nwp models and past weather observations, and interpret them together to come up with weather comments. to reproduce this, we use two types of meteorological data: a numerical forecast map and meteorological observation data. numerical forecast maps a numerical forecast map is composed of a sequence of 2d surface data extracted from the simulation results of an nwp model, which is a mathematical model of the atmosphere and oceans. in this study, we used numerical forecast maps around japan simulated using the global spectral model (gsm), which is an nwp model, provided by the japan meteorological agency 2 . the maps are updated four times a day at 0000, 0600, 1200, and 1800 japan standard time. the prediction of physical quantities such as humidity and temperature up to 84 hours ahead is available and is suitable for roughly determining weather trends over a few days. in the maps, grid points are set every 20 km in the range of 20 to 50 degrees north latitude and 120 to 150 degrees east longitude. therefore, the maps are composed of 151 × 121 grid points, where each point contains simulation results of the physical quantities corresponding to the area, such as 1021.01 hpa for air pressure. observation data since weather-forecast comments are often written in comparison with past weather (e.g., the day before), we also introduce meteorological observation data provided by the automated meteorological data acquisition system (amedas), managed by japan meteorological agency 3 . specifically, we use four physical quantities: precipitation, air temperature, wind speed, and sunshine duration. these quantities are sequential data observed every ten minutes at about 1300 stations across japan. ## weather-forecast generation we consider weather-forecast generation as a task of generating a description from a sequence of 2d data, which is a forecast map. since this can be viewed as video captioning @xcite , we first introduce an encoder-decoder model @xcite with an attention mechanism @xcite . figure 2 shows an overview of our proposed model. it takes three types of input data: a sequence of numerical forecast maps @xmath0 for the numerical maps, we use either an mlp or cnn to extract numeric features related to physical quantities, such as air pressure, and input each map to a bi-rnn to take their sequential information into account. we also incorporate meteorological observation data a and meta-data m by encoding with an mlp. we use the mlp to predict weather labels l from the output of the encoders. we use the rnn language model (rnnlm) @xcite to generate words w. we explain our proposed model and how we introduce meta-data and weather labels into it in the following sections. ## conclusion in this paper, we addressed the task of generating weather comments from meteorological simulations. we proposed a data-to-text model and incorporated three types of encoders for forecast maps, observation data, and meta-data into the model. in addition, we introduced weather labels representing the content of weather information to explicitly carry out content selection and improve the correctness of information in generated comments. experiments indicated that our model significantly improved the informativeness of generated comments and outperformed the baselines in both automatic and human evaluations. frank wilcoxon. 1945. individual comparisons by ranking methods. biometrics bulletin, 1(6):80-83. sam wiseman, stuart shieber, and alexander rush. 2017. challenges in data-to-document generation. in proceedings of the 2017 conference on empirical methods in natural language processing, pages 2253-2263. association for computational linguistics. li yao, atousa torabi, kyunghyun cho, nicolas ballas, christopher pal, hugo larochelle, and aaron courville. 2015. describing videos by exploiting temporal structure. in proceedings of the 2015 ieee international conference on computer vision, page 4507-4515. ieee computer society.