<prompt> Etsa lenaneo la ho lepa boemo ba leholimo ba machaba.nan  <response>: Instruction in English: Design a program for forecasting international weather. . Response in English: Here is a high-level program design for forecasting international weather:1. Data Collection: The first step in the program would be to gather data on current weather conditions and historical trends from various sources such as meteorological departments, satellite imagery, radar systems, and ground-based sensors. Weather data from major cities and regions across the world would be collected on parameters such as temperature, humidity, wind speed, atmospheric pressure, precipitation, and cloud cover, among others.2. Data Preprocessing: After data collection, the program would preprocess the data to remove any inconsistencies, outliers, or missing values to ensure that the data fed into the model is clean and reliable.3. Feature Engineering: The next step would be to engineer relevant features that would aid in weather forecasting. These features would be derived from the existing data, such as using historical weather data to calculate moving averages, or combining different weather parameters to create composite variables.4. Model Selection and Training: The program would use machine learning techniques to forecast weather conditions. Various algorithms such as decision trees, random forests, or neural networks could be used, and the most suitable model would be selected through a process of cross-validation and performance evaluation. The model would then be trained on the preprocessed and feature-engineered data.5. Forecast Generation: The trained model would be used to generate weather forecasts for different cities and regions across the world. These forecasts would be updated in real-time as new data is collected and fed into the model. The program would provide predictions on temperature, rainfall, wind speed, and other weather parameters for different time horizons, such as daily, weekly, or monthly forecasts.6. Presentation and Visualization: The final step would be to present the forecast data in an easy-to-understand format for the end-users. The program could leverage data visualization techniques to display weather data on maps or graphs, allowing users to quickly get an overview of the weather conditions in different parts of the world.This program design would provide a robust and reliable framework for forecasting international weather and could be further refined and improved over time as more data becomes available. . Response in Sesotho: Mona ke moralo oa maemo a holimo oa ho lepa boemo ba leholimo ba machaba: 1. Pokello ea Lintlha: Mohato oa pele lenaneong e tla ba ho bokella lintlha tsa maemo a leholimo a hajoale le mekhoa ea nalane ho tsoa mehloling e fapaneng joalo ka mafapha a boemo ba leholimo, litšoantšo tsa satellite. , litsamaiso tsa radar, le lisensara tse thehiloeng fatše. Lintlha tsa boemo ba leholimo tse tsoang litoropong tse kholo le libaka lefatšeng ka bophara li tla bokelloa ho latela maemo a kang mocheso, mongobo, lebelo la moea, khatello ea sepakapaka, pula, le maru, hara tse ling. 2. Pokello ea Boitsebiso: Ka mora ho bokelloa ha data, lenaneo le ne le tla lokisa data esale pele ho tlosa ho se lumellane hofe kapa hofe, li-outliers, kapa litekanyetso tse sieo ho netefatsa hore data e fepetsoeng mofuteng e hloekile ebile e ka tšeptjoa. 3. Boenjineri ba Tšobotsi: Mohato o latelang e tla ba ho etsa likarolo tse amehang tse tla thusa ho lepa boemo ba leholimo. Likarolo tsena li tla nkuoa ho data e teng, joalo ka ho sebelisa lintlha tsa nalane ea boemo ba leholimo ho bala lipalo-palo tse sisinyehang, kapa ho kopanya maemo a leholimo a fapaneng ho theha mefuta e fapaneng. 4. Khetho le Koetliso ea Moetso: Lenaneo le tla sebelisa mekhoa ea ho ithuta ka mochini ho lepa maemo a leholimo. Mekhoa e fapaneng e kang lifate tsa liqeto, meru e sa reroang, kapa marang-rang a marang-rang a ka sebelisoa, me mohlala o loketseng ka ho fetisisa o tla khethoa ka mokhoa oa ho netefatsa le ho hlahloba tsebetso. Joale mohlala o ne o tla koetlisetsoa ho data e ntlafalitsoeng le e entsoeng ka likarolo. 5. Moloko oa Forecast: Moetso o koetlisitsoeng o tla sebelisoa ho hlahisa ponelopele ea boemo ba leholimo bakeng sa litoropo le libaka tse fapaneng lefatšeng ka bophara. Likhakanyo tsena li ne li tla ntlafatsoa ka nako ea nnete ha data e ncha e ntse e bokelloa le ho kenngoa moetsong. Lenaneo le ne le tla fana ka likhakanyo tsa mocheso, pula, lebelo la moea, le maemo a mang a leholimo bakeng sa nako e fapaneng, joalo ka bolepi ba letsatsi le letsatsi, beke le khoeli kapa khoeli le khoeli. 6. Tlhahiso le Ponahalo: Mohato oa ho qetela e tla ba ho hlahisa lintlha tsa ponelopele ka mokhoa o utloisisehang habonolo bakeng sa basebelisi ba ho qetela. Lenaneo le ka sebelisa mekhoa ea pono ea data ho bontsa lintlha tsa boemo ba leholimo limmapeng kapa lirapeng, ho lumella basebelisi ho fumana kakaretso ea maemo a leholimo libakeng tse fapaneng tsa lefatse. Moralo ona oa lenaneo o tla fana ka moralo o matla le o tšepahalang oa ho lepa boemo ba leholimo ba machaba me o ka ntlafatsoa le ho ntlafatsoa ha nako e ntse e feta ha data e ntse e fumaneha. 