Theskill withwhichacoupledocean‐atmospheremodelisabletopredictprecipitation overarangeoftime scales (days to months) is analyzed. For a fair comparison across the seamless range of scales, the verification is performed using data averaged over time windows equal in length to the lead time. At a lead time of 1 day, skill is greatest in the extratropics around 408‐608 latitude and lowest around 208, and has a secondary local maximum close to the equator. The extratropical skill at this short range is highest in the winter hemisphere, presumably due to the higher predictability of winter baroclinic systems. The local equatorial maximum comes mostly from the Pacific Ocean, and thus appears to be mostly from El Ni~ Oscillation (ENSO). As both the lead time and averaging window are simultaneously increased, the extratropical skill drops rapidly with lead time, while the equatorial maximum remains approximately constant, causing the equatorial skill to exceed the extratropical at leads of greater than 4 days in austral summer and 1 week in boreal summer. At leads longer than 2 weeks, the extratropical skill flattens out or increases, but remains below the equatorial values. Comparisons with persistence confirm that the model beats persistence for most leads and latitudes, including for the equatorial Pacific where persistence is high. The results are consistent with the view that extratropical predictability is mostly derived from synoptic-scale atmospheric dynamics, while tropical predictability is primarily derived from the response of moist convection to slowly varying forcing such as from ENSO.

Seamless Precipitation Prediction Skill in the Tropics and Extratropics

Weather is an essential part of everyone's life, but its dynamic nature makes weather forecasting difficult. In this research, we propose a system that makes daily weather predictions in Sedati, an administrative district in the Sidoarjo Regency, Indonesia. The system uses Gated Recurrent Unit (GRU) algorithm that is fed with historical weather data from the BMKG Juanda Meteorological Station. On the test data, the GRU regression model resulted in the best average RMSE for four days' worth of prediction with humidity, wind speed, temperature, sea level pressure, maximum wind speed, minimum temperature, maximum temperature, and dew point at 3.7325; 1.31; 0.64; 0.85; 2,065; 0.7125; 0.6825; and 0.705. However, the average RMSE score of those variables after we integrated the model with local IoT devices were 5.6; 2.3; 1.56; 0.57; 2.47; 3.04; 1.78; and 0.55, respectively. The sequence GRU model for rain classification achieved an accuracy of 0.88, and the accuracy after the model was integrated with the IoT devices was 0.75. 

Realtime Weather Prediction System Using GRU with Daily Surface Observation Data from IoT Sensors

Weather is an essential part of everyone's life, but its dynamic nature makes weather forecasting difficult. In this research, we propose a system that makes daily weather predictions in Sedati, an administrative district in the Sidoarjo Regency, Indonesia. The system uses Gated Recurrent Unit (GRU) algorithm that is fed with historical weather data from the BMKG Juanda Meteorological Station. On the test data, the GRU regression model resulted in the best average RMSE for four days' worth of prediction with humidity, wind speed, temperature, sea level pressure, maximum wind speed, minimum temperature, maximum temperature, and dew point at 3.7325; 1.31; 0.64; 0.85; 2,065; 0.7125; 0.6825; and 0.705. However, the average RMSE score of those variables after we integrated the model with local IoT devices were 5.6; 2.3; 1.56; 0.57; 2.47; 3.04; 1.78; and 0.55, respectively. The sequence GRU model for rain classification achieved an accuracy of 0.88, and the accuracy after the model was integrated with the IoT devices was 0.75.

Water conservation is very necessary to support the creation of clean water quality that is free from harmful substances that can disturb the environment. So a system is needed to monitor water quality to determine the level of pollution that occurs. This system will work to see water quality in real time with several quality parameters such as pH, temperature, and water turbidity. The purpose of this research is to produce a predictive model and find out the prediction results of a data mining-based system. The method used to predict water quality uses the Seasonal Autoregressive Integrated Moving Average (SARIMA) method, because the water quality data is thought to contain seasonal patterns. The results of this study indicate that the SARIMA model can be applied to the dataset used and obtain the accuracy of the forecasting results on each of the tested parameter data. The results of water quality forecasting with this parameter are the result data for testing at a dataset of a depth of 30 cm and a depth of 60 cm for temperature parameters, namely MSE<0.1, and RMSE<0.02. For pH parameters, MSE<0.1, and RMSE<0.1. As well as the turbidity parameter, the results of MSE<0.02, and RMSE<0.13. From these results indicate that this system can predict water quality with past data.

Prediksi Kualitas Air Menggunakan Metode Seasonal Autoregressive Integrated Moving Average (SARIMA)

One of the impacts of the threat caused by heavy rain is flooding, which can have negative effects on human life. There are many factors that contribute to heavy rain, and predicting the intensity of rainfall issued by BMKG (Meteorology, Climatology, and Geophysics Agency) is an initial solution for planning and taking actions to mitigate the impacts of natural disasters. Machine learning methods can be used to predict weather parameters, especially time series rainfall. Deep learning, a branch of machine learning that can understand patterns and make weather parameter predictions with high accuracy, includes several algorithms commonly used for analyzing and predicting weather parameters, namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). This research aims to compare both algorithms and determine which one performs best in predicting rainfall at the North Lampung Geophysics Station. From the evaluation results with RMSE (Root Mean Square Error) value of 16.81, MSE (Mean Square Error) value of 282.55, and MAD (Mean Absolute Deviation) value of 10.43, it is known that the LSTM model 1 with a dataset split of 7:3 has the best performance in predicting rainfall. As for the rain prediction, the GRU model 1 with a dataset split of 7:3 performs best with an accuracy value of 62%, precision of 58%, recall of 66%, and f1score of 62%.

Perbandingan Long Short Term Memory (LSTM) dan Gated Recurrent Unit (GRU) Untuk Memprediksi Curah Hujan

Saat ini metode deep learning dapat diaplikasikan untuk memprediksi suatu kejadian, seperti memprediksi cuaca suatu wilayah. Salah satu contoh deep learning yang cocok digunakan pada jenis data time series adalah LSTM. Penelitian ini menerapkan metode deep learning LSTM dengan jumlah layer 200, perbandingan data training dengan data test sebesar 9:1, serta mengukur nilai RMSE dan RMSE update hasil validasi dan prediksi beberapa hari ke depan. Data yang digunakan terdiri dari pengukuran suhu udara, kelembaban udara, kecepatan angin, dan tekanan udara selama bulan Januari dan Februari 2019. Data bulan Januari digunakan sebagai data training dan test untuk melakukan validasi prakiraan, sedangkan data bulan Februari digunakan sebagai pembanding dari hasil prediksi deep learning LSTM. Hasil penelitian menunjukkan RMSE seluruh validasi parameter cuaca nilainya semakin baik ketika menggunakan LSTM dengan update . Diperoleh RMSE update untuk parameter suhu, kelembaban, kecepatan angin, dan tekanan udara masing-masing bernilai 0,576; 2,8687; 2,1963; dan 1,0647. Sedangkan prediksi suhu udara, kelembaban, kecepatan angin, dan tekanan udara untuk 1 hari ke depan (1 Februari 2019) masing-masing sebesar 1,0337; 6,3413; 2,8934; dan 1,4313. Dari parameter tersebut hanya parameter suhu dan kelembaban udara yang mengalami pertambahan RMSE seiring bertambahnya waktu. Sedangkan parameter kecepatan angin dan tekanan udara mengalami penurunan di hari ketiga dan meningkat secara kontinu hingga satu bulan ke depan. Today, deep learning can be applied to predict any events, such as predict the weather of a region. One of them is LSTM which is suitable for use in time series data types. This study conducted the deep learning LSTM with the number of 200 layers, ratio training with test data of 9:1, measuring the value of validation RMSE and RMSE update and also predictions some weather parameters in a few days later. The data used consisted of measurements of air temperature, humidity, wind speed, and air pressure during January and February 2019. The data January were used as training and test data to conduct forecast validation, while the data February was used as a comparison of the results predicted for deep learning LSTM. The result shows that the forecast RMSE for all-weather parameters is better when using LSTM with an update. Obtained for temperature, relative humidity, wind speed, and air pressure have RMSE with update are 0,576; 2,8687; 2,1963; and 1,0647, respectively. While the prediction of air temperature, wind speed, and air pressure for one day later (1 February 2019) is 1.0337; 6.3413; 2,8934; and 1.4313, respectively. From the all-weather parameters only temperature and humidity parameter that increase in RMSE over time. While the parameters such as wind speed and air pressure decreased on the third day and increased continuously for the next one month.

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Prediksi parameter cuaca menggunakan deep learning long-short term memory (lstm)

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Prediksi parameter cuaca menggunakan deep learning long-short term memory (lstm)