How is precipitation predicted and how can we use satellites and gauges such as Climate Prediction (CPC) Formation Algorithm (CMORPH), Global Precipitation Mapping Satellite (GSMaP), Tropical Precipitation Measuring Mission (TRMM), and some other satellites; Precipitation analysis (TMPA) used?
Precipitation is one of the important components of the global water cycle and is related to atmospheric circulation
In climate and climate change that is used for weather forecasting, hydrological process modeling, disaster monitoring, etc. Because precipitation varies widely in space and time, it is accurate and reliable. Higher temporal and spatial precipitation products are needed for stakeholder decision-making. Local-scale decision-making is needed. Precipitation data can be both temporally and spatially. presented a series of spatial and temporal events indicating the tendency to increase rainfall in a particular region and the spatio-temporal distribution directly affecting the availability
Water sources in rivers or watersheds. This availability of precipitation data is an important part of hydrological analysis, however, its inclusion is often insufficient and incomplete due to several factors such as the lack of both spatial and temporal observation data. Precipitation time series data, uneven number Precipitation stations, a limited number of observers and system observations, as well as manual data entry. this is . It is also difficult to obtain surface precipitation in real time. Observational data, which require preliminary investigation before they can be used directly. However, there is a need for accurate spatio-temporal and long-term precipitation data in climate change prediction, simulation study, hydrological forecasting, floods, landslides, droughts, disasters.
Management and investigation of water resources Several factors that contribute to uncertainty, such as observation errors, boundary errors or initial conditions, model or system errors, scale differences and unknown parameter heterogeneity, have a significant impact on mass and distributed hydrological performance. models. Usually, the highest amount of precipitation is considered. An important meteorological input in hydrological and water quality studies is accurate measurement of precipitation. For reliable and consistent hydrological forecasts, the quantity and quality of water, the accuracy of precipitation data, is needed.
including intensity, duration, geographic patterns and extent, significant impact on land surface output and hydrological models. has it. Large-scale hydrological models often rely on remotely sensed precipitation data from satellite sensors due to the lack of ground-sensing equipment and rain gauge networks. Gauges or satellites show regional and temporal variability and measurement errors although ground sensor
Networks such as rain gauges and radars provide the most amount. Direct observations of surface precipitation and frequent
They provide measurements with high time frequency
The systems have significant drawbacks. Gauges limited to
Point-scale observations, but they are also susceptible
Misleading readings due to wind and evaporation effects.
In addition, spatial interpolation of point-based observations
Adds uncertainty to the final grid in addition to measurement errors
Spatial precipitation dataset
The distribution and density of gauges are critical factors
Adequacy measurement has been shown by several studies to be fragmented
And irregular rain gauge networks have a significant impact
It can be based on the uncertainty of the hydrological model and that uncertainty
It decreases with increasing densitometer or optimization
distribution pattern. ground radar
On the other hand, networks often provide continuous
Spatial coverage with high spatial and temporal resolution.
However, their accuracy is affected by signal attenuation and
Extinction, surface scattering, illumination and effects, and
Uncertainty in reflectivity-rainfall-rate relationship
The latest technologies, such as remote sensing technology,
It can overcome the lack or unavailability of precipitation data
In the previous period, this means through satellite
The possibility of obtaining precipitation data remotely
measurements, thereby simplifying the collection process
At any time and from any region, satellites generally have several
Advantages over surface observation rain stations
The measurement of precipitation amounts is one of the above
spatial and temporal resolution with a wide coverage area,
Near real-time data, continuous recording, quick access, weather
effects, less field variability and easy data collection
Because of the free download now, there are several satellite-based precipitation products available, each of which is different. Degrees of accuracy of the Climate Prediction Center (CPC)
Formation Algorithm (CMORPH), Global Precipitation Mapping Satellite (GSMaP), Tropical Precipitation Measuring Mission (TRMM), Multisatellite Precipitation Analysis (TMPA) and others.
How precipitation is predicted and how to use satellites and gauges such as Climate Prediction Formation (CPC), Global Precipitation Mapping Satellite (GSMaP), Tropical Rainfall Measuring Mission (TRMM) and some other satellites. Precipitation analysis (TMPA) is used?
Precipitation is one of the important components of the global water cycle and is related to atmospheric circulation.
in climate and climate change used for weather forecasting, hydrological process modeling, disaster monitoring, etc. Because precipitation varies greatly in space and time, it is accurate and reliable. Higher spatial and temporal precipitation products are needed for stakeholder decision making. Local decision-making is needed. Precipitation data can be both temporal and spatial. It presented a set of spatial and temporal events that indicate the tendency for rainfall to increase in a particular region and the spatio-temporal distribution that directly affects availability.
Water sources in rivers or watersheds. The availability of precipitation data is an important part of hydrological analysis, however, its inclusion is often insufficient and incomplete due to several factors such as the lack of spatial and temporal observation data. Precipitation time series data, uneven number of precipitation stations, limited number of observers and system observations, as well as manual data entry. it is . It is also difficult to obtain surface precipitation in real time. Observational data that require preliminary investigation before direct use. However, in climate change prediction, simulation study, hydrological forecasting, floods, landslides, droughts, disasters, there is a need for accurate spatio-temporal and long-term precipitation data.
Management and investigation of water resources. Several factors that contribute to uncertainty, such as observation errors, boundary errors or initial conditions, model or system errors, scale differences and heterogeneity of unknown parameters, have a significant impact on mass and scattered hydrological performance. models. It is usually considered to be the highest amount of rainfall. An important meteorological input in hydrological and water quality studies is the accurate measurement of precipitation. For reliable and consistent hydrological forecasts, water quantity and quality, the accuracy of precipitation data is required.
including intensity, duration, geographic patterns and extent, significant impact on land surface output and hydrological models. has it. Large-scale hydrological models often rely on remotely sensed precipitation data from satellite sensors due to the lack of ground sensing equipment and rain gauge networks. Gauges or satellites show regional and temporal changes and measurement errors even though the ground sensor
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