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Depending on the data record producer, different product requirements may be applied and they are used to evaluate validation results. An often-used way to handle this is to define several levels of requirements where each level is linked to specific needs or priorities. A three-level approach like the following is rather common:

Satellite product levels

Satellite-based products are often described as belonging to the following condensed description of processing levels, each one with different complexity and information content:

A more comprehensive definition of all processing levels is given here: https://www.earthdata.nasa.gov/engage/open-data-services-and-software/data-information-policy/data-levels.

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Key Performance Indices (KPIs) for ICDR products to be produced for the extension of the TCDR (so far covering the period January 2019 until June 2022) have been defined using MODIS Level 3 products as reference observations. The KPI test is based on a binomial test against low (2.5%) and high (97.5%) percentiles of the MODIS-CLARA difference distribution. The low and high percentiles are given in the following table:

An extensive description of past, current and future availability of data from the Advanced Very High Resolution Radiometer (AVHRR) is given. In addition, future prospects of utilizing AVHRR-heritage spectral channel data from new imaging sensors on new satellites are described. It is concluded that the AVHRR-based observations series, based on one morning and one afternoon orbit constellation, can be prolonged to reach at least 60 years if adding AVHRR-heritage information. However, for this to become effective, efforts are needed to harmonize and homogenize observations between true AVHRR data and AVHRR-heritage data. This concerns both calibration aspects and spatial resolution aspects. Regarding the development of retrieval methods, it is noted that the access to high quality cloud observations from active sensors (especially CALIPSO-CALIOP data) has played an important role in advancing both retrieval methods and methods for uncertainty estimations in recent years. The access to the high-quality reference data has been especially important for the development of Bayesian and artificial neural network (ANN) based retrievals. A future continuation of active observations from space is judged as crucial for further development of retrieval methods based on AVHRR-heritage data.

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For the evaluation of the ICDR, corresponding products from the MODIS instrument (MODIS Collection 6.1) are used as a reference. The distribution of the de-seasonalised differences between MODIS products and the CLARA.A2.1 TCDR has been compiled and the corresponding 2.5 and 97.5 percentile differences are given in Table 2-3.

These percentiles are used to check, by means of a binomial test at 5 % significance level, whether the corresponding ICDR differences are consistent with the TCDR differences or not. Further details on these tests are found in the Report on Updated KPIs (D10).

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2.1.6 Data format and content issues

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\mu m

\mu m

In order to monitor the performance of the SLSTR extension ICDR, new KPI values were also generated from comparisons of the Cloud Properties TCDR ESA AATSR to the NASA MODIS Cloud Properties product. These comparisons are represented as the 2.5 and 97.5 percentiles of the distribution of differences between (A)ATSR monthly-mean values and the corresponding MODIS values (corrected for the mean seasonal cycle), and are summarised in Table 2-6.

\sigma_{std}^{2} = \frac{1}{N}\sum\limits_{i=1}^{N}(x_{i} - \langle x \rangle)^{2} \ \ (Eq 2)

\sigma_i

\langle \sigma \rangle

\langle \sigma \rangle = \frac{1}{N}\sum\limits_{i=1}^{N}(\sigma) \ \ (Eq 3)

\sigma_{prop}

\sigma_{prop}^{2} = \frac{1}{N} \langle \sigma_{i}^{2} \rangle \ \ (Eq 4)

\sigma_{corr}

\sigma_{corr}^{2} = \sigma_{std}^{2} - (1-c) \sqrt{\langle \sigma_{i}^{2} \rangle - \langle \sigma_{i} \rangle^{2}} \ \ (Eq 5)