Global Modeling and Assimilation Office Goddard Space Flight Center National Aeronautics and Space Administration Recent Advances towards the Assimilation of Cloud-Affected Infrared Radiances in the GSI Will McCarty NASA Goddard Space Flight Center Global Modeling and Assimilation Office 2015 JCSDA Workshop 14 May 2015

Global Modeling and Assimilation Office Goddard Space Flight Center National Aeronautics and Space Administration Introduction Traditionally, only those infrared radiance observations which are unaffected by clouds are assimilated – Observations that are either cloud-free or sensitive only to the atmosphere above the cloud – This methodology discards ~80% of surface-sensitive observations In an attempt to further exploit the large quantities of discarded data, the GSI has been extended to assimilate cloud-affected infrared radiances – These observations have a strong sensitivity to cloud top temperature Previous efforts have focused specifically on AIRS, but now the method has been extended to all hyperspectral infrared sensors (AIRS Aqua, CrIS SNPP, IASI Metop-A, IASI Metop-B)

Global Modeling and Assimilation Office Goddard Space Flight Center National Aeronautics and Space Administration Assimilation Methodology The approach has three main steps beyond typical clear-sky radiance assimilation – A cloud height must be accurately determined – The forecasted brightness temperature (F in O-F) and Jacobians must be recalculated to account for the cloud (cloudy radiative transfer) – In addition to the surface and atmospheric fields (T, q, O 3 ), cloud height is also treated as a control (sink) variable, and the Jacobians w.r.t. cloud height must be calculated Step One – Cloud Height Retrieval – The GSI uses a minimum residual method (Eyre and Menzel 1989) to determine cloud height for cloud screening of clear measurements – This same method is used for the cloud-affected radiance assimilation, with two exceptions: the retrieval extended to interpolate between layers The retrieval is used only at the first outer loop; the solution of the cloud height sink variable is used in the second outer loop

Global Modeling and Assimilation Office Goddard Space Flight Center National Aeronautics and Space Administration Assimilation Methodology Step Two –Cloudy radiative transfer and jacobians – Using the cloud height, the forecasted T B is recalculated based on the retrieved cloud height and fraction (graybody assumption) – Jacobians are adjusted to move sensitivity below the cloud to the cloud surface Step Three – Calculate jacobian w.r.t cloud height – Analytically derived from the RT equation Known Limitations – Cloud height retrieval accuracy – Graybody assumption – specifically, single, homogeneous cloud

Global Modeling and Assimilation Office Goddard Space Flight Center National Aeronautics and Space Administration Comparing the minimum residual technique on AIRS measurements to both MODIS and CALIPSO Level-2 Cloud Height products, it is seen that there is often disagreement in cloud height, but both have shortcomings – MODIS is sensitive to many of the same shortcomings as AIRS – CALIPSO has sampling issues (note the longer time period of comparison) 1 Jul-30 Sept Jul 2013 – 28 Feb 2014 Cloud Height Retrieval Accuracy

Global Modeling and Assimilation Office Goddard Space Flight Center National Aeronautics and Space Administration In clear-sky radiances, it is seen that when there is heterogeneity, particularly with overlapping clouds, that O-F is biased, and the adjoint-derived observation impact metric, which is a measure of 24hr forecast accuracy projected into observation space, indicates forecast degradation (positive) T High → Low → Window H 2 Ov4μm T Impact per Ob O-F MODIS  CTP (Low – High, hPa) Effects of Heterogeneity

Global Modeling and Assimilation Office Goddard Space Flight Center National Aeronautics and Space Administration Advancing the Assimilation Methodology Cloud-affected infrared radiances are considered as a second stream of data – Each AIRS, CrIS, and IASI footprint is considered twice – once for clear- sky assimilation and once for cloudy assimilation Data in the GSI is thinned to a 145 km thinning grid – Data selection on this grid is key to increasing yields – For clear-sky assimilation, many tests are performed to get the clearest footprint available – For cloudy assimilation, the “cloudiest” footprint is desired – but what does that mean? It is noted here that “selected” means data that gets past thinning. Data that is “used” is actively assimilated. Selected data can still be excluded by quality control

Global Modeling and Assimilation Office Goddard Space Flight Center National Aeronautics and Space Administration Data Thinning and Selection For AIRS and CrIS, the coldest footprint, as determined by a 11 μm window brightness temperature, is selected – This skews selection to heterogeneous scenes, as a flat cloud will be warmer than a flat cloud that is under thin cirrus The IASI data streams (BUFR) include AVHRR cloud information – The number of cloud levels, determined by a clustering algorithm, and their fractional coverage is reported – Clear-sky data thinning uses this data to avoid clouds – Now, the cloudy radiance assimilation can use this to increase the likelihood of selecting homogeneous cloud scenes Simply exclude all observations that have more than two cloud clusters This would make sense for clear observations as well

Global Modeling and Assimilation Office Goddard Space Flight Center National Aeronautics and Space Administration All Selected Data – Cloudy Stream Selected Data All Selected Data – Clear Stream By design, the distributions are shifted towards higher cloud retrievals for all four instruments – In cloudy situations, AIRS and CrIS behave more like each other than the IASI instruments – The comparison of cloud fractions for the two streams is not shown, but are consistent in that cloud fraction increases in the cloudy stream

Global Modeling and Assimilation Office Goddard Space Flight Center National Aeronautics and Space Administration All Selected Data – Cloudy Stream Selected Data All Selected Data – Cloudy Stream In cloudy selection, IASI is shifted towards opaque, which CrIS and AIRS both are more flat across all fractions – Though non-physical, cloud fractions greater than 1.0 can be retrieved, though they cannot be assimilated – In this approach, opaque observations are considered ideal, so skewing the distribution towards opaque is ideal

Global Modeling and Assimilation Office Goddard Space Flight Center National Aeronautics and Space Administration Once considering only those that fall within certain thresholds of cloud fraction (0.9 – 1.0) and cloud height ( hPa), observation only a small fraction of observations considered are retained – The increase generally ranges an increase of 4-8% of total radiances, but these percentages can mean different (i.e. no WV channels in cloudy) All Selected Data – Cloudy Stream Selected Data and Used Data All Used Data – Cloudy Stream

Global Modeling and Assimilation Office Goddard Space Flight Center National Aeronautics and Space Administration Spatial Distribution of Assimilated Data AIRS Ch 123 CrIS Ch 123 IASI Metop-A Ch 205 IASI Metop-B Ch 205

Global Modeling and Assimilation Office Goddard Space Flight Center National Aeronautics and Space Administration O-F Distribution Clearly skewed distribution across all instruments – Better gross checks needed to eliminate long tail Shift in mode, larger cold tail for AIRS vs other instruments The yield of IASI is still larger, near the mode, but the tails are larger – Improvement near the mode shows potential benefit of cluster analysis – The overall increase in yield is also do to more loose gross checks Further evaluation of methodology necessary – Clear issue regarding outliers – gross check needs to be made consistent – Even if the skewedness of the tails is removed, the mode is still biased cold – this can potentially impact variational bias correction – The initial development has used consistent bias correction w/ clear and cloudy observations to prevent drift ~840 cm -1 (~12 μm)

Global Modeling and Assimilation Office Goddard Space Flight Center National Aeronautics and Space Administration Clearly skewed distribution across all instruments – Better gross checks needed to eliminate long tail Shift in mode, larger cold tail for AIRS vs other instruments The yield of IASI is still larger, near the mode, but the tails are larger – Improvement near the mode shows potential benefit of cluster analysis – The overall increase in yield is also do to more loose gross checks O-F Distribution Further evaluation of methodology necessary – Clear issue regarding outliers – gross check needs to be made consistent – Even if the skewedness of the tails is removed, the mode is still biased cold – this can potentially impact variational bias correction – The initial development has used consistent bias correction w/ clear and cloudy observations to prevent drift ~840 cm -1 (~12 μm)

Global Modeling and Assimilation Office Goddard Space Flight Center National Aeronautics and Space Administration AIRS Ch 123 CrIS Ch 123 Spatial Distribution of O-F AIRS Ch 123 CrIS Ch 123 Clear Cloudy

Global Modeling and Assimilation Office Goddard Space Flight Center National Aeronautics and Space Administration O-F Variance There is a larger uncertainty in the cloudy radiances than clear – Projection of cloud height assessment errors – Spectral dependence on ratio of cloud to clear lower wavenumbers see above clouds Emissivity of cloud is assumed constant An inflation of σ o is needed – In addition to larger errors, erroneous height assignment will introduce cross-channel correlated errors – Initial approach was multiplicitave σ o inflation (colored lines above), but it conflicted with the gross check

Global Modeling and Assimilation Office Goddard Space Flight Center National Aeronautics and Space Administration Future Work Pre-screening at the thinning stage is a concern for yield – Cluster information seems beneficial – Using some sort of MODIS/VIIRS information, whether clustering or Level 2 product information, could be helpful – Still a concern with heterogeneity assessment if I’m only given an imager cloud height and fraction – data streams have been or are being considered – The difference of AIRS vs. other instruments is concerning – Quality control applied equally across the footprint is necessary Observation Errors need additional assessment – Multiplicative amplification of σ o may not make sense since clear-sky values are so different for the three instruments – There may be a need to consideration of cloud height in error assessment sink variable error is already height dependent correlation of calculated cloudy observations probably varies w/ height