WRF Sensitivity for Seasonal Climate Simulations of Precipitation Fields on the CORDEX South America Domain

Dynamic numerical models of the atmosphere are the main tools used for weather and climate forecasting as well as climate projections. Thus, this work evaluated the systematic errors and areas with large uncertainties in precipitation over the South American continent (SAC) based on regional climate simulations with the weather research and forecasting (WRF) model. Ten simulations using different convective, radiation, and microphysical schemes, and an ensemble mean among them, were performed with a resolution of 50 km, covering the CORDEX-South America domain. First, the seasonal precipitation variability and its differences were discussed. Then, its annual cycle was investigated through nine sub-domains on the SAC (AMZN, AMZS, NEBN, NEBS, SE, SURU, CHAC, PEQU, and TOTL). The Taylor Diagrams were used to assess the sensitivity of the model to different parameterizations and its ability to reproduce the simulated precipitation patterns. The results showed that the WRF simulations were better than the ERA-interim (ERAI) reanalysis when compared to the TRMM, showing the added value of dynamic downscaling. For all sub-domains the best result was obtained with the ensemble compared to the satellite TRMM. The largest errors were observed in the SURU and CHAC regions, and with the greatest dispersion of members during the rainy season. On the other hand, the best results were found in the AMZS, NEBS, and TOTL regions.

Restaurant Sales Prediction Using Machine Learning

In general, the revenue forecast, offer information, and the weather gauge setting will record an accurate estimate of any restaurant's future revenue. The turnover is significantly focused on the need of the customers. Either way, the performance has transformed over the past couple of years with the presentation of huge amounts of information and calculations during the time taken to gain the upper hand. It is fundamental to learn and understand the importance of the information that will be used in any business process. Again, climate forecasting can be done alongside business expectations with the organization.

Improved trend-aware post-processing of GCM seasonal precipitation forecasts

Climate trends have been observed over the recent decades in many parts of the world, but current global climate models (GCMs) for seasonal climate forecasting often fail to capture these trends. As a result, model forecasts may be biased above or below the trendline. In our previous research, we developed a trend-aware forecast post-processing method to overcome this problem. The method was demonstrated to be effective for embedding observed trends into seasonal temperature forecasts. In this study, we further develop the method for post-processing GCM seasonal precipitation forecasts. We introduce new formulation and evaluation features to cater for special characteristics of precipitation amounts, such as having a zero lower bound and highly positive skewness. We apply the improved method to calibrate ECMWF SEAS5 forecasts of seasonal precipitation for Australia. Our evaluation shows that the calibrated forecasts reproduce observed trends over the hindcast period of 36 years. In some regions where observed trends are statistically significant, forecast skill is greatly improved by embedding trends into the forecasts. In most regions, the calibrated forecasts outperform the raw forecasts in terms of bias, skill, and reliability. Wider applications of the new trend-aware post-processing method are expected to boost user confidence in seasonal precipitation forecasts.

Unprecedented Climate Change in India and a Three-Pronged Method for Reliable Weather and Climate Prediction

India, one of the most disaster-prone countries in the world, has suffered severe economic losses as well as life losses as per the World Focus report.1 More than 80% of its land and more than 50 million of its people are affected by weather disasters. Disaster mitigation necessitates reliable future predictions, which need focused climate change research. From the climate change perspective, the summer monsoon, the main lifeline of India, is predicted to change very adversely. The duration of the rainy season is going to shrink, and pre-monsoon drying can also occur. These future changes can impact the increase of vector-borne diseases, such as malaria, dengue, and others. In another recent study, 29 world experts from various institutions found that the largest exposure to disasters, such as tropical cyclones (TCs), river floods, droughts, and heat waves, is over India. For improved and skillful prediction, we suggest a three-stage cumulative method, namely, K is for observational analysis, U is for knowledge and understanding, and M is for modeling and prediction. In this brief note, we report our perspective of imminent weather disasters to India, namely, monsoons and TCs, and how the weather and climate forecasting can be improved, leading to better climate change adaptation.

Plan Across Borders

This chapter looks at promising regional cooperation efforts to de-escalate tensions heightened by climate change. Tackling problems like pandemics or climate change within the framework of traditional jurisdictional boundaries means that policymakers continue to treat these challenges like matters of domestic or local concern, rather than the transboundary threats that they are. Breaking down these barriers requires deep focus on cross-border solutions. For example, the climate change problem of “too little and too much water” demands transboundary consideration of evolving conditions in river basins and ocean fisheries. Risk reduction efforts that stretch across regions also offer good avenues for building disaster preparedness, including stockpiling, creating insurance risk pools, setting up systems for regional climate forecasting and early warning, and re-energizing multilateralism. Likewise, the most urgent transborder challenge of all, climate-induced migration, calls for ever greater global cooperation—not less.

Soft Periodic Convolutional Recurrent Network for Spatiotemporal Climate Forecast

Many machine-learning applications and methods are emerging to solve problems associated with spatiotemporal climate forecasting; however, a prediction algorithm that considers only short-range sequential information may not be adequate to deal with periodic patterns such as seasonality. In this paper, we adopt a Periodic Convolutional Recurrent Network (Periodic-CRN) model to employ the periodicity component in our proposals of the periodic representation dictionary (PRD). Phase shifts and non-stationarity of periodicity are the key components in the model to support. Specifically, we propose a Soft Periodic-CRN (SP-CRN) with three proposals of utilizing periodicity components: nearby-time (PRD-1), periodic-depth (PRD-2), and periodic-depth differencing (PRD-3) representation to improve climate forecasting accuracy. We experimented on geopotential height at 300 hPa (ZH300) and sea surface temperature (SST) datasets of ERA-Interim. The results showed the superiority of PRD-1 plus or minus one month of a prior cycle to capture the phase shift. In addition, PRD-3 considered only the depth of one differencing periodic cycle (i.e., the previous year) can significantly improve the prediction accuracy of ZH300 and SST. The mixed method of PRD-1, and PRD-3 (SP-CRN-1+3) showed a competitive or slight improvement over their base models. By adding the metadata component to indicate the month with one-hot encoding to SP-CRN-1+3, the prediction result was a drastic improvement. The results showed that the proposed method could learn four years of periodicity from the data, which may relate to the El Niño–Southern Oscillation (ENSO) cycle.

PROBLEMS FACED BY THE RICE AND MAIZE GROWERS DUE TO CLIMATE CHANGE, MITIGATION AND ADAPTATION MEASURES UNDERTAKEN

A research study was conducted in the state of Arunachal Pradesh in order to identify the various problems faced by the rice (Oryza sativa L.) and maize (Zea mays L.) grower in the state due to climate change and the various mitigation and adaptation measures undertaken by them in view of the problems faced by them. The study was carried out for the time period from 1987 to 2018 in two districts viz. East Siang and Lohit of Arunachal Pradesh; being the highest producing district of rice and maize in the state respectively. The study showed that the respondents perceived climate change and reported to have observed a change in the timing and duration of rainfall received along with changes in temperature. They reported various problems faced by them in the duration of the study while ranking decreasing yield as the most important problem faced by them, followed by pest and disease infestation and weed infestation in the fields. The farmers also adopted various means in order to counter the problems faced due to climate change like changing the cropping time and pattern, introducing climate resilient varieties and switching to more economically profitable crops. In view of the observations made during the study, some policies and future course of actions suggested for the problems faced by the farmers can include adoption of sustainable and diversified form of agriculture, involvement of Government, cooperative and self help groups to reduce price risk. Farmers can adopt water saving technologies like controlled irrigation, development of crop monitoring, climate forecasting and mapping the climate susceptible areas are the immediate need of the hour.

Investigating long-term variations in cloud base height over Sofia, Bulgaria

<p> </p><p>Clouds have a key role in weather and climate forecasting due to their effect on global radiation and water budget. Clouds change the radiation energy in the Earth-atmosphere system by reducing both incoming and outgoing parts, depending on their macro- and microphysical characteristics such as cloud base height (CBH), optical properties etc. These clouds properties are generally related to cloud types, so the effects in weather and climate caused by various cloud types differ greatly. It is known that high clouds cause the earth's surface to heat up, while low clouds cause cooling. Obviously, cloud radiation forcing is an important source of uncertainty in the numerical weather and climate models, so the registered and expected changes in the properties of clouds due to a warming climate need in-depth studies. But cloud base height is not only important for weather and climate forecasting, but also for airplane traffic safety.  Nowadays, retrieving the CBH is mainly based on satellite and ground-based observations. Satellite-borne instruments provide tempting spatial coverage but uncertainty in CBH estimation should be considered. In contrast, many ground-based observations of the CBH are characterized by higher accuracy. Nowadays, ceilometers - lidars specifically designed to detect CBH, that operate continuously and unattended, providing high vertical and time-resolution data, are reference instrument in CBH measurement. In addition, rawinsondes provide in-situ measurements of temperature, humidity, and pressure, so that the CBH can be evaluated by the lifting condensation level or by threshold value in relative humidity. In areas where only surface measurements are available, a simple adiabatic model of a rising air parcel can be applied in the CBH assessment. In this work, based on ceilometer, rawinsonde and surface measurements, the characteristics of CBH over Sofia, Bulgaria are studied in detail. We start with an intercomparison between CBHs obtained from three types of ground-based observations, considering the individual advantages and disadvantages of the methods by using ceilometer as reference. Finally, the daily, seasonal and interannual variability of CBH over Sofia are interpreted.</p>

Development of a Real-Time Surface Solar Radiation Measurement System Based on the Internet of Things (IoT)

The determination of the levels of solar radiation incident on the terrestrial surface (W·m−2) is essential for several areas such as architecture, agriculture, health, power generation, telecommunications, and climate forecasting models. The high cost of acquiring and maintaining radiometric equipment makes it difficult to create and expand monitoring networks. It contributes to the limited Brazilian radiometric network and affects the understanding and availability of this variable. This paper presents the development of a new surface solar radiation measurement system based on silicon photodiodes (Si) with a spectral range between 300 nm and 1400 nm incorporating Internet of Things (IoT) technology with an estimated cost of USD 200. The proposed system can provide instantaneous surface solar radiation levels, connectivity to wireless networks and an exclusive web system for monitoring data. For the sake of comparison, the results were compared with those provided by a government meteorology station (INMet). The prototype validation resulted in determination coefficients (R2) greater than 0.95 while the statistical analysis referred to the results and uncertainties for the range of ±500 kJ·m−2, less than 4.0% for the developed prototypes. The proposed system operates similarly to pyranometers based on thermopiles providing reliable readings, a low acquisition and maintenance cost, autonomous operation, and applicability in the most varied climatological and energy research types. The developed system is pending a patent at the National Institute of Industrial Property under registration BR1020200199846.