Convective Rainfall Rate PGE05 SAFNWC   Table of contents   1.- Goal of CRR product  2.- CRR algorithm summary description  3.- List of inputs for CRR  4.- Description of CRR outputs 5.- Example of CRR visualisation   Access to "Algorithm Theoretical Basis Document for ”Convective Rainfall Rate” (CRR - PGE05 v3.0)" for a more detailed description.

1.- Goal of CRR product

           The objective of the CRR product is to estimate the precipitation rate associated to convective clouds. This product provides to forecasters complementary information to other SAF NWC products related to rain and convection monitoring as PGE04 (Precipitating clouds) and PGE02 (Cloud type).

 

2.- CRR algorithm summary description

            The algorithm developed for the NWCSAF CRR product assume that clouds being both high and with large vertical extent are more likely to be raining, so that R = f(IR,VIS), being R the rainfall intensity expressed  in mm/h. By other side, the IR-WV brightness temperature difference is a useful parameter for extracting deep convective clouds with heavy rainfall (Kurino, T., 1996).

            The basic CRR mm/h value for each pixel is obtained from calibration matrices. The CRR calibration matrices are different if the software will use the solar channel or not. If a pixel belongs to a day mask and the solar channel is used the basic CRR calibration data is a 3-D matrix which uses the following bands: IR10.8, WV6.2 and VIS0.6. If a pixel belongs to a night mask or to a day mask but not using solar channel the basic calibration values are stored in a 2-D matrix and only two bands are used: IR10.8 and WV6.2. When the software uses the solar channel the normalised visible reflectances are obtained by dividing by the cosine of solar zenith angle.

            The calibration method, based on Rainsat techniques, tries to establish a statistical relationship between VIS reflectances, IR and WV temperatures and the rainfall rates derived from radar data. In summary, a composite radar data were compared pixel by pixel with a geographically matched MSG data in the same resolution and the total rain rate were calculated as a function of the two or three variables (IR brightness temperatures, IR-WV brightness temperature differences and normalised VIS reflectances). The radar data are used only for training the system and are not used directly as part of the output product.

            The retrieval of the basic CRR can be latitude dependant. In this case 2-D and 3-D difference matrices, made with the differences between the elements of Nordic and Spanish matrices, are needed in order to obtain the amount that must be added to the basic Spanish value, to obtain the latitude corrected value.

            In a second phase, a filtering process is performed in order to eliminate stratiform rain data which are not associated with convective clouds: the obtained basic CRR data are set to zero if all the nearest pixels in a grid of selected semisize (def. value: 3pix) centred on the pixel do not have an equal or higher value than a selected threshold (def. value: 3mm/h). The size of the grid and the filter threshold can be modified by the user through the configuration model.

            To take into account the temporal and spatial variability of the cloud tops, the amount of moisture available to produce rain and the influence of orographic effects on the precipitation distribution, several correction factors can be applied to the basic CRR value by the users. So that, the possible correction factors are the moisture correction, the cloud top growth/decaying rates or evolution correction, the cloud top temperature gradient correction  (Gilberto et all, 1998) and the orographic correction (Gilberto et all, 1999).

• Moisture correction factor

            This factor has been defined as the product of Precipitable Water, PW,  in the layer from the surface to 500 hPa and the Relative Humidity, RH, (mean values between the surface and the 500 hPa level) data, obtained from a numerical model. The PWRH factor takes values from 0.0 to 2.0, and the environment is considered dry if PWRH is significantly lower than 1.0 and quite moist if PWRH is greater than 1.0.

• Cloud growth rate correction factor

            Convective rain is assumed to be associated with growing clouds exhibiting overshooting tops. Consecutive satellite IR images are used to indicate vertically growing and decaying cloud systems.

            The cloud growth correction factor, also designated as evolution correction factor, only changes the magnitude of the rain rate through a coefficient if the analysed pixel becomes warmer in the second image.

• Cloud-top temperature gradient correction factor

            The cloud growth rate correction factor can not be applied when consecutive images are not available. In this case the alternative method of Cloud-top Temperature Gradient Correction is applied.

This correction factor, also designated as gradient correction factor, is based on a search of the highest (coldest) and lowest (less cold) cloud tops. The idea is to search for the pixels that are below the average cloud top surface temperature (local temperature minima) and assume these pixels indicate active convection associated with precipitation beneath.

The hessian of the temperature field is analysed for each pixel with a temperature lower than 250K, in order to search for those pixels with extreme values as is explained in the Algorithm Theoretical Basis Document[enlace]. Different coefficients will be applied modifying the rain rate corresponding to those pixels which have a maximum (meaning that are warmer than its surroundings) and those ones which have neither a local IR temperature maximum nor minimum. Otherwise rain rate is not modified.

• Parallax correction

To apply the orographic correction factor is necessary to know the exact cloud position with respect to the ground below. This is not a problem when a cloud is located directly below the satellite; however, as one looks away from the sub-satellite point, the cloud top appears to be farther away from the satellite than the cloud base. This effect increases as you get closer to the limb and as clouds get higher.

When the Parallax Correction is working, a spatial shift is applied to every pixel with precipitation according to the basic CRR value.

• Orographic correction

Rainfall amounts are dependent on the atmospheric flow over the mountains and on the characteristics of the flow disturbances created by the mountains themselves.

This correction factor uses the interaction between the wind vector (corresponding to 850 hPa level from the NWP) and the local terrain height gradient in the wind direction to create a multiplier that enhances or diminishes the previous rainfall estimate, as appropriate.

 

3.- List of inputs for CRR
 

         Dynamic inputs:

• Satellite imagery:

The following SEVIRI brightness temperatures and visible reflectances and are needed at full IR spatial resolution   

T10.8mm	TPrev10.8mm	T6.2mm	VIS0.6mm
Mandatory	Optional*	Mandatory	Optional

The SEVIRI channels are input by the user in HRIT format and extracted on the desired region by SAFNWC software package.

* If TPrev10.8?m is not available, the Cloud Growth Rate Correction Factor can not be computed but the Cloud-top Temperature Gradient Correction Factor is computed instead as an alternative 

o       Numerical model:

    This information is mandatory for moisture and orographic corrections. When this information is not available, CRR is computed without applying these two corrections.

    Parallax correction can run without the NWP parameters using the climatological profile.

    For moisture correction:

 Relative Humidity at 1000, 925, 850, 700 and 500 hPa

             Dew Point temperature at 2 m

             Temperature at 2 m

             Temperature at 1000, 925, 850, 700, 500 hPa

 Surface Pressure

    For parallax correction:

 Temperature at 1000, 925, 850, 700, 500, 400, 300, 250 and 200 hPa

 Geopotential at 1000, 925, 850, 700, 500, 400, 300, 250 and 200 hPa

    For orographic correction:

 U and V wind components in 850 hPa

        Static inputs:

o       Sun angles associated to SEVIRI imagery

    This information is mandatory for normalising the VIS image when the solar channel is used. It is computed by the CRR software itself using the definition of the region and the satellite characteristics.

o       Ancillary data sets:

-Basic calibration matrices (3-D, 2-D) are available in the SAFNWC software package and are needed by the CRR software.

-Difference matrices (3-D, 2-D) are available in the SAFNWC software package and they are mandatory if latitude correction is required. Users can choose which matrices will the SW use trough the model configuration file.

-Saturation Vapour table is mandatory for Humidity correction.

-Saturation Vapour Polynomial Coefficients table is mandatory for Humidity correction.

-Climatological profile is mandatory for Parallax correction.

-Elevation mask is mandatory for orographic correction.

o       Model configuration file for PGE05:

    The CRR model configuration file contains configurable system parameters in the product generation process related with algorithm thresholds, ancillary datasets, numerical model data, corrections to be applied, etc.

 

            4.-Description of CRR outputs

                        CRR product is coded in HDF5 format and its content is the following:  

• CRR classes:

                        The rainfall rates obtained by the CRR algorithm expressed in mm/h are converted into eleven classes as it is shown bellow: 

 

• CRR hourly accumulations:        

              Rainfall rates from the images in the last hour are used in order to compute the hourly accumulation. This output is expressed in mm and includes a palette that uses the same colours as the classes output palette.

• CRR intensity in mm/h:   

              Rainfall rates in mm/h are necessary to calculate the hourly accumulation. This is the reason for the existence of this output. It is not intended to be used as a Nowcasting tool, therefore it has no palette.

• CRR_QUALITY:

              7 bits mask indicating which corrections have been applied for each pixel. Moreover, it indicates whether the product is latitude dependant or not and if the SEVIRI solar channel has been used during the computation of the CRR:

1 bit for moisture correction:

0: No correction

1: Corrected by PWHR factor

1 bit for cloud growth rate correction:

0: No correction

1: Corrected by IR data from previous slot

1 bit for cloud top temperature correction:

0: No correction

1: Corrected by IR temperature gradient

1 bit for parallax correction:

0: No correction

1: Corrected by parallax

1 bit for orographic effect correction:

0: No correction

1: Corrected by orographic effects

1 bit for latitude dependant:

0: No latitude dependant

1: Latitude dependant

1 bit for solar channel used:

0: No solar channel used

1: Solar channel used

• CRR_DATAFLAG:

                        8 bits mask indicating the processing status of each pixel:

1 bit for IR10.8, WV6.2 or VIS0.6 data missing

0: All the channel data required are available

1: There is a missing data in some channel

1 bit to indicate if the set of SEVIRI data is out of the calibration matrices range

0: The set of SEVIRI data is contained in the calibration matrices range

1: The set of SEVIRI data is out of the calibration matrices range

1 bit to identify mathematical errors

0: No mathematical error

1: A mathematical error has occurred

1 bit for the convective filter

0: The CRR value remains the same

1: The CRR value has been set to zero because of the filtering process

1 bit for the filled holes after parallax correction

0: No hole due to the parallax correction

1: Hole due to the parallax correction filled by a median filter

2 bits the hourly accumulation CRR band status

0: All required bands were available

1: One previous CRR band is missing

2: At least two previous CRR bands are missing (no consecutive)

3: At least two previous CRR bands are missing (some are consecutive)

1 bit for the status of the CRR pixels used to compute the hourly accumulation

0: All the pixels used in the computing of the hourly accumulation have their CRR_DATAFLAG bits set to 0

1: At least one of the pixels used in the computing of the hourly accumulation has at least one of its CRR_DATAFLAG bits set to 1

            5.- Example of CRR visualisation

                      Instantaneous Rates       

          Below is shown an image corresponding to CRR classes output. It has been obtained at full resolution and all corrections have been applied.

                                                                                    Figure 1. CRR classes output corresponding to 11^th September 2008 at 16:00Z.

                       Hourly Accumulations

           Below is shown an image corresponding to CRR hourly accumulations output. It has been obtained at full resolution and all corrections have been applied

                                                                                    Figure 2. CRR hourly accumulations output corresponding to 11^th September 2008 at 16:30Z

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-         Algorithm Theoretical Basis Document for ”Convective Rainfall Rate” (CRR - PGE05 v3.0), 2009

-         Product User Manual for “Convective Rainfall Rate” (CRR - PGE05 v3.0), 2009

-         Validation Report for “Convective Rainfall Rate” (CRR - PGE05 v3.0), 2009