Evaluations of the Climatologies of Three Latest Cloud Satellite Products Based on Passive Sensors (ISCCP-H, Two CERES) against the CALIPSO-GOCCP

In this study, the climatologies of three different satellite cloud products, all based on passive sensors (CERES Edition 4.1 [EBAF4.1 and SYN4.1] and ISCCP–H), were evaluated against the CALIPSO-GOCCP (GOCCP) data, which are based on active sensors and, hence, were treated as the reference. Based on monthly averaged data (ocean + land), the passive sensors underestimated the total cloud cover (TCC) at lower (TCC < 50%), but, overall, they correlated well with the GOCCP data (r = 0.97). Over land, the passive sensors underestimated the TCC, with a mean difference (MD) of −2.6%, followed by the EBAF4.1 and ISCCP-H data with a MD of −2.0%. Over the ocean, the CERES-based products overestimated the TCC, but the SYN4.1 agreed better with the GOCCP data. The ISCCP-H data on average underestimated the TCC both over oceanic and continental regions. The annual mean TCC distribution over the globe revealed that the passive sensors generally underestimated the TCC over continental dry regions in northern Africa and southeastern South America as compared to the GOCCP, particularly over the summer hemisphere. The CERES datasets overestimated the TCC over the Pacific Islands between the Indian and eastern Pacific Oceans, particularly during the winter hemisphere. The ISCCP-H data also underestimated the TCC, particularly over the southern hemisphere near 60° S where the other datasets showed a significantly enhanced TCC. The ISCCP data also showed less TCC when compared against the GOCCP data over the tropical regions, particularly over the southern Pacific and Atlantic Oceans near the equator and also over the polar regions where the satellite retrieval using the passive sensors was generally much more challenging. The calculated global mean root meant square deviation value for the ISCCP-H data was 6%, a factor of 2 higher than the CERES datasets. Based on these results, overall, the EBAF4.1 agreed better with the GOCCP data.

The MOPITT Version 9 CO Product: Sampling Enhancements and Validation

Characteristics of the Version 9 (V9) MOPITT ("Measurements of Pollution in the Troposphere") satellite retrieval product for tropospheric carbon monoxide (CO) are described. The new V9 product includes many CO retrievals over land which, in previous MOPITT product versions, would have been discarded by the cloud detection algorithm. Globally, the number of daytime MOPITT retrievals over land has increased by 30–40 % relative to the Version 8 product, although the increase in retrieval coverage exhibits significant geographical variability. Areas benefiting from the improved cloud detection performance include (but are not limited to) source regions often characterized by high aerosol concentrations. The V9 MOPITT product also incorporates a modified calibration strategy for the MOPITT near-infrared (NIR) CO channels, resulting in greater temporal consistency for the NIR-only and thermal infrared-near infrared (TIR-NIR) retrieval variants. Validation results based on in-situ CO profiles acquired from aircraft in a variety of contexts indicate that retrieval biases for V9 are typically within the range of ±5 % and are generally comparable to results for the V8 product.

Modeling the Microwave Transmissivity of Row Crops

We develop a method to model the microwave transmissivity of row crops that explicitly accounts for their periodic nature as well as multiple scattering. We hypothesize that this method could eventually be used to improve satellite retrieval of soil moisture and vegetation optical depth in agricultural regions. The method is characterized by unit cells terminated by periodic boundary conditions and Floquet port excitations solved using commercial software. Individual plants are represented by vertically oriented dielectric cylinders. We calculate canopy transmissivity, reflectivity, and loss in terms of S-parameters. We validate the model with analytical solutions and illustrate the effect of canopy scattering. Our simulation results are consistent with both simulated and measured data published in the literature.

NWP verification against own analysis by using a Data Assimilation confidence mask

&lt;p&gt;Numerical Model Prediction (NWP) verification against station measurements from a surface network is affected by sub-tile representativeness issues. Moreover, the station network is often not representative of the whole verification domain (e.g. usually coastal stations are predominant) and large unpopulated regions (such as oceans, Polar regions, deserts) are under-sampled. Verification against gridded analyses mitigate these issues, since they partially address the sub-tile representativeness, and sample homogeneously the verification domain. Moreover, gridded analyses merge station network measurements to radar and satellite retrieval estimates, in a physical coherent fashion, over the same NWP grid. Verification against own analysis, despite quite convenient, is however hampered by its dependence on the NWP background model, which renders the verification &amp;#8220;incestuous&amp;#8221;, further than being affected by the uncertainties introduced by retrieval algorithms and Data Assimilation (DA) procedures.&lt;/p&gt;&lt;p&gt;In this study we investigate the use of a gridded NWP own analysis for verification, by applying a mask to reduce the background model contribution. The mask weights the verification scores to account for the amounts of observations assimilated and their associated uncertainty, as estimated from DA. We illustrate the approach by using the Canadian Precipitation Analysis (CaPA), which assimilates station measurements, radar and satellite-based (IMERG) observations. The CaPA confidence (weighting) mask is dynamic and changes depending on the daily available (assimilated) observations, and on their corresponding DA error statistics; it is defined as&lt;/p&gt;&lt;p&gt;&amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160;mask = 1 - var(A-O)/var(B-O)&lt;/p&gt;&lt;p&gt;where A=analysis, B=Background, O=observations. Where the analysis is identical to the background model, the weighting mask is zero.&lt;/p&gt;&lt;p&gt;We evaluate the Canadian Regional Deterministic Prediction System (RDPS), which is the NWP system used as background model for CaPA. As expected, the verification results obtained by using the weighting mask lay between the verification results obtained verifying against the analysis over the full domain, and the results obtained verifying against station measurements.&amp;#160;The effects of sub-tile representativeness are quantified by comparing verification results against station measurements to verification results against CaPA for the grid-points co-located with the stations. Finally, the comparison of the verification results against CaPA over the full domain versus the verification results against CaPA for the grid-points co-located with stations, estimates to which extent the station network is representative of the full domain.&lt;/p&gt;&lt;p&gt;The approach aims to propose a simple -yet effective- better practice for verification against own analysis.&lt;/p&gt;

Systematic detection of local CH₄ anomalies by combining satellite measurements with high-resolution forecasts

In this study, we present a novel monitoring methodology that combines satellite retrievals and forecasts to detect local CH4 concentration anomalies worldwide. These anomalies are caused by rapidly changing anthropogenic emissions that significantly contribute to the CH4 atmospheric budget and by biases in the satellite retrieval data. The method uses high-resolution (7 km × 7 km) retrievals of total column CH4 from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor satellite. Observations are combined with high-resolution CH4 forecasts (∼ 9 km) produced by the Copernicus Atmosphere Monitoring Service (CAMS) to provide departures (observations minus forecasts) at close to the satellite's native resolution at appropriate time. Investigating these departures is an effective way to link satellite measurements and emission inventory data in a quantitative manner. We perform filtering on the departures to remove the synoptic-scale and meso-alpha-scale biases in both forecasts and satellite observations. We then apply a simple classification scheme to the filtered departures to detect anomalies and plumes that are missing (e.g. pipeline or facility leaks), underreported or overreported (e.g. depleted drilling fields) in the CAMS emissions. The classification method also shows some limitations to detect emission anomalies only due to local satellite retrieval biases linked to albedo and scattering issues.

Validation of Satellite-Retrieved CCN based on a Cruise Campaign over the polluted Northwestern Pacific Ocean

&lt;p&gt;&lt;span&gt;In this study, a methodology for satellite retrieval of cloud condensation nuclei (CCN) in shallow marine boundary layer clouds is presented and validated. This methodology is based on retrieving cloud base drop concentration (N&lt;sub&gt;d&lt;/sub&gt;) and updrafts (W&lt;sub&gt;b&lt;/sub&gt;), which are used for calculating supersaturation (S). The N&lt;sub&gt;d&lt;/sub&gt; is the activated CCN concentration in clouds at a given S. The accuracy of the satellite retrieval is validated against the surface-measured CCN of a cruise campaign over the heavily polluted northwest Pacific Ocean. Clouds which are coupled with the sea surface have good agreement between satellite retrieved N&lt;sub&gt;d&lt;/sub&gt; and surface-measured CCN after performing corrections for temperature and adiabatic fraction. This study broadens the applicability of the methodology from aerosol-limited to contaminated regions. The validation shows &amp;#177;30% accuracy in retrieving CCN of both clean and polluted regions. The results further demonstrate the strong dependence of marine shallow cloud N&lt;sub&gt;d&lt;/sub&gt; on CCN number concentrations and updraft, which allows us to further apply this methodology to quantify the relationships between CCN and cloud microphysical properties and reduce the uncertainty of radiation forcing caused by aerosol cloud interaction (ACI).&lt;/span&gt;&lt;/p&gt;