Evaluation of a Forward Operator to Assimilate Cloud Water Path Into WRF-DART

2272 MONTHLY WEATHER REVIEW VOLUME 141

Evaluation of a Forward Operator to Assimilate Cloud Water Path into WRF-DART

THOMAS A. JONES Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma

DAVID J. STENSRUD NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

PATRICK MINNIS NASA Langley Research Center, Hampton, Virginia

RABINDRA PALIKONDA Science Systems and Applications, Inc., Hampton, Virginia

(Manuscript received 17 August 2012, in ﬁnal form 18 January 2013)

ABSTRACT

Assimilating satellite-retrieved cloud properties into storm-scale models has received limited attention despite its potential to provide a wide array of information to a model analysis. Available retrievals include cloud water path (CWP), which represents the amount of cloud water and cloud ice present in an integrated column, and cloud-top and cloud-base pressures, which represent the top and bottom pressure levels of the cloud layers, respectively. These interrelated data are assimilated into an Advanced Research Weather Research and Forecasting Model (ARW-WRF) 40-member ensemble with 3-km grid spacing using the Data Assimilation Research Testbed (DART) ensemble Kalman filter. A new CWP forward operator combines the satellite-derived cloud information with similar variables generated by WRF. This approach is tested using a severe weather event on 10 May 2010. One experiment only assimilates conventional (CONV) observations, while the second assimilates the identical conventional observations and the satellite-derived CWP (PATH). Comparison of the CWP observations at 2045 UTC to CONV and PATH analyses shows that PATH has an improved representation of both the magnitude and spatial orientation of CWP compared to CONV. As- similating CWP acts both to suppress convection in the model where none is present in satellite data and to encourage convection where it is observed. Oklahoma Mesonet observations of downward shortwave flux at 2100 UTC indicate that PATH reduces the root-mean-square difference errors in downward shortwave flux 2 by 75 W m 2 compared to CONV. Reduction in model error is generally maximized during the initial 30-min forecast period with the impact of CWP observations decreasing for longer forecast times.

1. Introduction of nontraditional data sources such as observations from radars (e.g., Snyder and Zhang 2003) and satellites (e.g., The characterization of cloud properties in numerical Pincus et al. 2011) is needed. In particular, radar re- weather prediction (NWP) models plays an important ﬂectivity and radial velocity have been used to estimate role in forecasting convective systems. To reduce the the location of precipitation (and the corresponding uncertainty in analyzed cloud properties, the assimilation clouds) and sample the near-storm environment (e.g., Dowell et al. 2010). The assimilation of radar data has proven effective at increasing the accuracy of the model Corresponding author address: Dr. Thomas A. Jones, Cooperative analyses and downstream forecasts. Institute for Mesoscale Meteorological Studies, University of Oklahoma, and NOAA/OAR/National Severe Storms Labora- One disadvantage of radar data assimilation is that tory, 120 David L. Boren Blvd., Norman, OK 73072. precipitation radars are not very sensitive to nonprecipitating E-mail: [email protected] clouds. Yet it is important that nonprecipitating clouds

DOI: 10.1175/MWR-D-12-00238.1

Ó 2013 American Meteorological Society Unauthenticated | Downloaded 10/02/21 09:35 AM UTC JULY 2013 J O N E S E T A L . 2273 be properly analyzed, since they can affect energy bal- (CTT), cloud water path (CWP), cloud ice path (CIP), ances within the model and also indicate the locations of cloud optical thickness (COT), and cloud coverage (e.g., possible convective initiation (e.g., Mecikalski et al. Minnis et al. 2011). CWP represents the column-integrated 2013). Remote sensing observations from satellites cloud liquid and ice water from cloud base to cloud top, provide information on cloud properties on similar which is often defined in pressure levels [cloud-base horizontal and temporal scales as radar data, but with pressure (CBP) and cloud-top pressure (CTP; Minnis greater sensitivity to the non- or preconvective clouds et al. 2011)]. COT represents the optical extinction re- that may be present. The question then becomes how sulting from a cloud and is directly related to cloud best to assimilate these observations, since two different thickness (Kato et al. 2006). These products have the approaches to satellite data assimilation are in use. The advantage of being directly related to the cloud prop- first is the direct assimilation of satellite infrared and ertiesbeinganalyzedwithinNWPmodelsanddonot microwave radiances. Model state variables are gener- require the use of complex RTMs. One challenge that ally transformed into simulated satellite radiances via remains is the lack of vertical sensitivity of satellite sensors a radiative transfer model (RTM) built into the forward to cloud properties. For geostationary sensors in particu- operator (Vukicevic et al. 2004, 2006; McNally et al. 2006; lar, only cloud-top and sometimes cloud-base heights can Chen et al. 2008; Otkin 2010; Polkinghorne et al. 2010; be resolved. The vertical distribution of cloud micro- Polkinghorne and Vukicevic 2011). Assimilating radi- physical properties, such as cloud liquid water, cannot ances directly often has been the favored approach in currently be retrieved directly. However, satellite re- operations and research studies since it avoids uncer- trievals still provide a wide range of information on clouds tainties and discrepancies in the various retrieval algo- not generally available from other sources. rithms that differ from satellite to satellite (Derber and Satellite data assimilation on regional and smaller scales Wu 1998; Errico 2000). This technique generally per- has received much less attention than corresponding radar forms best in clear-sky regions, but it can be very resource data assimilation in part due to the difficulties inherent intensive depending on the number and resolution of in observing and assimilating cloud properties. There are channels being assimilated (Migliorini 2012). Recent re- many nonunique relationships between observed vari- search has begun to focus on assimilating cloudy radiances, ables and cloud properties and the relationships that do but this remains an even more challenging and resource exist are often highly nonlinear, violating many of the intensive task (Vukicevic et al. 2004, 2006; Errico et al. underlying error distribution assumptions used in several 2007; Weisz et al. 2007; Pavelin et al. 2008; Polkinghorne common forms of data assimilation (Pincus et al. 2011). et al. 2010; Polkinghorne and Vukicevic 2011). No single solution exists to these difficulties, but recent Despite the inherent challenges, which include un- research has taken steps to begin addressing this problem. certainties in the background error and covariance es- Lin et al. (2003) use satellite cloud fraction and cloud-top timate for high-resolution cloudy radiances and pressure to adjust the humidity fields within the model uncertainties in the relationship between radiance ob- where clouds are detected. For example, atmospheric servations and the three-dimensional (3D) microphysi- water vapor is adjusted to a saturated value at that level cal structures of clouds, positive results have been found (and below) where a cloud exists. Above this layer, the when assimilating these data. Vukicevic et al. (2004, water vapor is reduced if it is saturated within the model. 2006), Polkinghorne et al. (2010), and Polkinghorne and Similarly, if a satellite failed to detect a cloud and the Vukicevic (2011) found that assimilating these data us- model analysis shows a saturated environment, the model ing a four-dimensional (4D) variational technique cou- water vapor content is reduced. Following this theme, pled with an RTM improved the characterization of Yucel et al. (2002, 2003) proposed a more complex tech- cloud properties within high-resolution NWP analysis nique to adjust model cloud (and humidity) variables and forecasts. In particular, the 3D distribution of cloud toward the satellite observed cloud properties using a ice mass was improved significantly while liquid water four-step process. Step 1 determines where both satellite cloud mass below ice layers was less affected. While very and model data indicate clear-sky conditions. In this promising, applying these techniques within an ensem- case, no changes to the model analysis are made. Step 2 ble modeling framework proved too resource intensive determines where both model and satellite indicate for this research requiring a different approach to be clouds. Here, the total column cloud liquid water and ice used. from satellite measurements are used to adjust the cor- A more resource-friendly and easier-to-interpret ap- responding values in the model, with the model-analyzed proach to satellite data assimilation is to use retrieved vertical distribution of cloud water/ice used to guide products (Migliorini 2012). For clouds, these products can the fit to the total column cloud water reported by the include cloud-top pressure (CTP), cloud-top temperature satellite. This assumes the model vertical distribution of

Unauthenticated | Downloaded 10/02/21 09:35 AM UTC 2274 MONTHLY WEATHER REVIEW VOLUME 141 cloud properties is correct even if the total column on board the GOES-East (GOES-13) satellite using concentrations are off. Step 3 represents the case where retrieval algorithms developed for the Clouds and the no clouds are detected by satellite yet the analysis con- Earth’s Radiant Energy System project (Minnis et al. tains a cloud. In this case, all model-analyzed cloud 2008a,b, 2011). These algorithms are currently capable properties are set to zero. Finally, step 4 is the case of retrieving CTP, CBP, CWP, COT, and cloud phase where the satellite detects a cloud not present in the among other properties. model analysis. This is the most challenging case, as it The NWP model and assimilation approach chosen are requires the total column satellite-derived cloud prop- the Advanced Research Weather Research and Fore- erties are assimilated on to the three-dimensional model casting Model (ARW-WRF; Skamarock et al. 2008) in grid. conjunction with the Data Assimilation Research Test- Benedetti and Janiskova (2008) and Lauwaet et al. bed (DART) software (Anderson et al. 2009). To the (2011) use satellite COT within a forward operator de- authors’ knowledge, actually assimilating the values of signed to determine the best model profiles of column- CWP, CTP, and CBP for real cases on a storm-scale grid integrated cloud water and cloud ice that fit their has not been previously attempted. As a result, this re- respective observations through either best-fit or itera- search requires the development and testing of a forward tive algorithms. This represents a more direct assimi- operator to transform WRF and DART hydrometeor lation approach than described by Yucel et al. (2002, variables into a parameter that can be compared against 2003), but relies heavily on the assumptions made within GOES satellite retrievals (Pincus et al. 2011). the forward operator to derive the vertical profiles of To assess the viability of assimilating CWP, a southern cloud properties from their column-integrated obser- plains severe weather event occurring on 10 May 2010 is vations. Results from these studies clearly indicate that selected for study and two data assimilation experiments assimilating cloud properties in some form improves the are conducted for comparison. One assimilates only cloud analysis fields as well as the energy balance and conventional observations and is used as a control. The precipitation forecasts. Results also indicate that the more second assimilates the identical conventional observa- frequent the assimilation step, the greater the potential tions plus CWP every 15 min over a 3-h window using improvement. the newly developed forward operator described in Many uncertainties and questions remain as to the section 4. Results from these experiments are compared best cloud properties and assimilation techniques to use during both the assimilation cycle and a 90-min forecast when attempting to correctly characterize clouds within period to determine the potential for satellite CWP data high-resolution NWP models. While it would be im- to increase skill in storm-scale forecasting. Oklahoma possible to provide closure to all these issues in a single Mesonet data represent the primary verification obser- study, we hope to provide some answers by assimilat- vations for this study (McPherson et al. 2007). ing Geostationary Operational Environmental Satel- The characteristics of the 10 May 2010 case study lite (GOES) imager cloud property retrievals into a event are described in section 2. Section 3 provides in- convective-scale NWP model using an ensemble Kalman formation on the GOES-based cloud retrievals. The filter (EnKF) approach. The primary advantage of this WRF-DART characteristics and the assimilation tech- approach is that it provides a flow-dependent and dy- niques are outlined in section 4. Section 5 describes the namically evolving estimate of the background error co- observation diagnostics of the assimilation, with sections variance for assimilated observations (Kalman 1960). 6 and 7 providing an overview and verification of the This is especially important for satellite-derived cloud analysis fields. Summary and conclusions are given in properties as they are a function of the observing condi- section 8. tions and the actual characteristics of the cloud, both of which can vary significantly with space and time (e.g., 2. Event summary: 10 May 2010 Polkinghorne et al. 2010). The EnKF approach also reduces the reliance on uniform error variance character- A surface low pressure center deepens as it moves istics required by some other assimilation techniques eastward through southern Kansas, trailing a dryline (Heemink et al. 2001). southward into central Oklahoma at 2100 UTC 10 May Finally, cloud ice or liquid phase, determined for the 2010. Convective available potential energy (CAPE) 2 top of the cloud, is also available. For this study, CWP is exceeds 2500 J kg 1 and storm relative helicity (SREH) 2 the primary variable to be assimilated, while CTP and exceeds 500 m2 s 2 throughout much of central and CBP are used to define the vertical extent of the cloud southeastern Oklahoma at this time, indicating an en- layer during the assimilation process. The cloud prop- vironment favorable for tornadic supercells. Thunder- erties used in this research are derived from the imager storms develop ahead of the eastward progressing

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;488648. The GOES imager measures atmospheric radiances in ﬁve spectral bands: a 1-km visible channel at 0.65 mm, a 4-km shortwave infrared channel at 3.9 mm, a 4-km water vapor channel at 6.5 mm, a 4-km atmospheric infrared window channel at 10.8 mm, and an

8-km CO2 absorption channel at 13.3 mm (Menzel and Purdom 1994; Schmit et al. 2001). a. Retrieval algorithm Cloud properties are retrieved from 4-km GOES imager data for pixels classified as cloudy. Pixels are determined to be clear or cloudy using the multispectral threshold retrieval algorithm developed by Minnis et al. (2008b) and adapted to geostationary data (Minnis et al. 2008a). A variety of cloud parameters are retrieved using modified versions of the visible infrared shortwave-infrared split-window technique (VISST), and the shortwave-infrared infrared split-window technique (SIST; Minnis et al. 2008a,b, 2011). The first method is FIG.1.GOES-13 1-km resolution visible satellite imagery at # 8 2045 UTC 10 May 2010 with Weather Surveillance Radar-1988 used during the day (solar zenith angles 82 ), when Doppler (WSR-88D) reflectivity at 1 km AGL from the KTLX visible spectrum data are available. Otherwise the latter is radar overlaid. Also shown are the severe weather reports (red 5 used. The SIST was modified to accommodate the re- tornado, green 5 hail, blue 5 wind) that occur during this event placement of the 12-mm split-window channel on the between 2000 UTC 10 May and 0100 UTC 11 May. older GOES series (GOES-8 through GOES-12)withthe 13-mm channel the newer GOES series (GOES-13 and dryline in southern Kansas around 1800 UTC and in thereafter). For the altered SIST, the modified CO2- central and southern Oklahoma by 2100 and 2200 UTC, absorption technique (MCAT) of Chang et al. (2010a,b) respectively. Visible satellite imagery from GOES-13 at uses 10.8- and 13.3-mm channels to retrieve CTP, CTT, 2045 UTC shows a line of cumulus clouds in west-central and cloud emissivity for clouds having COT less than Oklahoma ahead of dryline that are in the process of ;4. The 3.9-mm channel is used to estimate the particle developing into thunderstorms. Farther north in Kansas, effective size. During the day, the MCAT is applied in- a fully developed supercell is suggested from the large dependently of the VISST and its results replace those cirrus outflow present. Visible imagery also shows a large of the VISST for ice clouds having VISST COT , 4 and area of low-level stratus clouds in eastern Oklahoma and VISST CTP . MCAT CTP 1 200 hPa. Otherwise, the the adjacent regions of Kansas and north Texas (Fig. 1). algorithms are mostly the same as the original versions. Dozens of damaging wind, severe hail, and tornado re- Cloud-base pressure is the pressure corresponding to ports occur between 2000 UTC 10 May and 0100 UTC 11 the altitude equal to the difference between cloud-top May (Fig. 1). In contrast to the cloudy region east of the height and cloud thickness. Cloud thickness is parame- dryline, clear sky and dry air characterize the environ- terized in terms of CWP, CTT, ln(COT), and, for liquid ment behind the dryline in western Oklahoma, with some clouds, droplet effective radius (Minnis et al. 2010). dust even being blown in from west Texas. Hereafter, the combined retrieval algorithm is simply referred to as VISST. A full description of the unmodified VISST algorithm is found in Minnis et al. (2008b, 2011). 3. GOES imager products We have chosen to assimilate the retrieved values of The GOES imager takes multispectral images over CWP, CTP, and CBP at 15-min intervals at the nominal the Western Hemisphere every half hour with images 4-km resolution. over the continental United States (CONUS) occurring The retrieved values of CWP and CTP for 2045 every 5–15 min depending on the need (Menzel and UTC 10 May (Fig. 2) indicate that a large area of low-top Purdom 1994). This allows for high temporal sampling clouds (CTP . 800 hPa) exist over the eastern half of of a particular location, which represents a significant Oklahoma corresponding to the stratus field observed in advantage compared to polar-orbiting satellites that can Fig. 1. The accompanying CWP values are generally 2 only observe the same location twice per day. GOES-13 small being ,0.3 kg m 2. In the northern portion of the views the subject domain with a sensor zenith angle of domain, the retrieved CTP , 300 hPa corresponds with

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FIG. 2. (a) CWP and (b) CTP retrieved from GOES-13 data at 2045 UTC. Black dots denote pixels removed as being potentially dust contaminated. the cirrus outflow from the early convection with cor- present. This represents a challenge for data assimila- responding CWP values being much larger, in excess of tion purposes, since WRF resolves this distinction in 2 1.0 kg m 2. The line of cumulus ahead of the dryline is fine detail. One way to address this issue is to ignore the also evident from GOES CWP with small areas of ele- phase information from the satellite retrieval and as- vated CWP values present associated with developing sume that CWP be equal to the sum of all cloud water convection. regardless of the cloud phase. Hereafter, CWP from GOES refers to either CWP or CIP, and the phase flag in b. Retrieval uncertainties the retrieval is ignored. Comparison of GOES cloud-top and cloud-base CWP from VISST has been compared with a variety heights with surface-based cloud radar and lidar obser- of other types of measurements to help understand vations at the Atmospheric Radiation Measurement the uncertainties. Dong et al. (2002) found a 6% under- (ARM) Southern Great Plains (SGP) site near Lamont, estimate of the liquid water path from GOES-8 data Oklahoma, found that GOES cloud heights retrieved relative to microwave radiometer retrievals for overcast during daytime hours are generally accurate to within stratus clouds over the SGP site. For the same conditions 61.0 km with a slight low bias (Smith et al. 2008). Un- during a different period, Dong et al. (2008) found that certainties are greatest for thin cirrus clouds in the upper the CWP from VISST applied to Moderate Resolution troposphere and smallest for single layer low-level Imaging Spectroradiometer (MODIS) data differed from stratus. Cloud-base heights for deep convection are also the SGP microwave radiometer retrieval by 7%, on av- sometimes too high, since lower-level information can- erage. Similar differences were found for marine stratus not be ascertained because of the very opaque nature of scenes using CWP from the VISST applied to GOES-10 these clouds. Converting 61 km to pressure coordinates data and compared with three different satellite-borne yields an uncertainty of approximately 650 hPa for microwave imager retrievals (Painemal et al. 2012). For cloud heights in the midtroposphere. Height uncertainties thin ice clouds, Mace et al. (2005) determined that the increase in the presence of multilayer clouds with retrieved CWP retrieved from VISST applied to MODIS data is CTP and CBP being the most representative of the top- 9% greater than that from surface-based cloud radar data most cloud layer in the atmosphere. at the SGP. For thicker clouds over the CONUS, Smith Since cloud phase is a binary classification, the dis- et al. (2007) determined that the GOES-estimated CWP tinction between CWP and CIP is rather fuzzy for deep, for thick ice clouds is unbiased compared to CloudSat multiphase clouds and when multiple cloud layers are cloud radar retrievals for areas having snow-free surfaces.

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The instantaneous uncertainty is on order of a factor of 2, which is comparable to the uncertainty in any given radar retrieval algorithm (e.g., Stein et al. 2011). Over snow, the CWP tends to be overestimated using the limited number of channels available on the current GOES imagers. Waliser et al. (2009) showed that, over the globe, the mean VISST-derived CWPs for ice clouds from MODIS agree well spatially and in magnitude with those from CloudSat. The differences noted above can be considered as first- order estimates of the uncertainties in the VISST-derived CWP values. The uncertainties are likely smaller because the standard deviations of the differences include errors due to time and space sampling differences, errors in the microwave and radar retrieval methods, and calibration uncertainties. Removal of those error sources would reduce the instantaneous uncertainties. Assessing CWP uncertainties in optically thick ice clouds is difficult because those clouds are often associated with storms that FIG. 3. Mesoscale (domain 1) and nested grid convective scale are unfriendly to aircraft and have large internal vari- (domain 2) WRF domains used for both experiments. abilities in hydrometer size and concentration. Thus, it is not surprising that differences for thick ice clouds are around the 100% level. All of the retrievals mentioned Milbrandt–Yau cloud microphysics scheme (Skamarock above are for single-layer, vertically contiguous clouds. et al. 2008; Milbrandt and Yau 2005). The ensemble Greater errors are likely to be realized for multilayered, consists of 40 members that use the same physical pro- multiphase clouds (e.g., Minnis et al. 2007). cess parameterization schemes, yet have different initial Finally, there is a potential for the retrieval algorithm to and boundary conditions. A mesoscale analysis is gen- misclassify certain high concentrations of aerosols, generated using a 15-km domain covering most of the erally dust, as clouds since the radiance signatures of both CONUS with 51 vertical levels stretching from the sur- can be similar (Brennan et al. 2005). Evidence of this face up to 50 hPa (Fig. 3). A parallel filter within the misclassification exists in western Oklahoma where CTP DART software assimilates atmospheric observations is retrieved in a region where no clouds are apparent on into the WRF using an ensemble adjustment Kalman the visible imagery (Figs. 1 and 2b). A close examination filter (Anderson et al. 2009). Initial and boundary con- of the visible data, along with meteorological knowledge ditions are obtained from the 1200 UTC 10 May 2010 of a dry atmosphere coupled with strong winds, indicates NAM model analysis and subsequent forecast fields the presence of atmospheric dust. As a result, the ‘‘cloud at 3-h intervals. The background error covariances are properties’’ retrieved in this region are likely in error. estimated by the National Meteorological Center (NMC) For this research, dust-contaminated pixels are defined method (Parrish and Derber 1992) using the WRF data as those whose (10.8–13.3 mm) brightness temperature assimilation software. These samples are then added to difference is less than 24 K and whose visible reflectance is each ensemble member to account for uncertainties in below 0.18. These thresholds are based on GOES obser- the initial and boundary conditions (Torn et al. 2006). vations of a similar dust plume on 9 April 2009, which is The mesoscale data assimilation cycle starts at 1200 UTC described in detail by Jones and Christopher (2010). Pixels 10 May 2010 and continues for 9 h until 2100 UTC using classified as dust are denoted in black in Fig. 2b for each conventional surface, marine, aircraft, and radiosonde event. Future cloud and aerosol retrieval algorithms based observations with hourly forecasts generated thereafter. on GOES-R data will offer more robust methods to re- Additional description of the model characteristics move spurious data than the simple technique applied here. used for the mesoscale assimilation can be found in Jones and Stensrud (2012). Using the hourly mesoscale analyses and forecasts 4. WRF-DART characteristics as boundary conditions, a 3-km one-way nested domain is then generated over the area where the most intense a. Run-time settings convection occurs (Fig. 3). WRF model characteristics The NWP model selected for this study is the ARW- remainthesameasforthemesoscalerunwiththe WRF model version 3.3.1 using the double-moment exception that no cumulus parameterization is used.

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Conventional observations and GOES CWP retrievals et al. 2010). Results indicate that the Milbrandt–Yau are assimilated at 15-min intervals beginning at 1800 UTC scheme generates the best representation of the observed and continuing until 2100 UTC. The error covariance ice properties from cirrus outflow compared to the other for all CWP observations is assumed to be constant with schemes tested (not shown), leading to the Milbrant–Yau 2 a value of 0.05 kg m 2. The horizontal localization half- scheme being selected. radius used by the covariance localization function is set b. CWP forward operator to 100 km for conventional observations and 20 km for satellite-derived observations (Gaspari and Cohn 1999). Assimilating satellite retrieved cloud properties such A similar horizontal localization for satellite observa- as CTP and CWP directly into DART requires the de- tions in cloudy regions was also applied by Vukicevic velopment of a new forward operator, with the main et al. (2004, 2006) with positive results. For conventional challenge being to translate from a total column CWP observations, a vertical localization half-radius of 4 km is value into a vertical distribution. A forward operator is applied. Since CWP is integrated over the entire atmo- created that integrates the model mixing ratios of cloud spheric column, a vertical localization is not applicable liquid water (QCLOUD), cloud ice (QICE), graupel to this variable. Adaptive covariance inflation is used in (QGRUAP), rain (QRAIN), and snow (QSNOW) fol- both data assimilation experiments. The predicted var- lowing the definition used by Otkin (2010). Both liquid iables updated by the data assimilation scheme include and frozen variables are summed into a single CWP the three wind components, perturbation temperature, variable to better approximate the characteristics of the perturbation geopotential, perturbation surface pres- satellite retrieval. Recall that the GOES-retrieval algo- sure of dry air, and potential temperature tendency due rithm only classifies a pixel as liquid or ice cloud based to microphysics as well as water vapor and hydrome- on the cloud height assumption. For mixed-phase clouds, teors. Also updated are the 10-m wind fields, 2-m tem- both ice and water are present in the retrieved value of perature and water vapor fields, total surface pressure CWP. variables, and finally soil moisture, which are diagnosed The vertical extent of the cloud layer at each obser- by the surface and boundary layer schemes using state vation location is defined by CTP and CBP and is passed variables on the model grid. to the forward operator and used to constrain the CWP A 3-h assimilation window is used to assimilate a calculated from the model. For example, if CTP 5 400 hPa reasonable time series of observations into the model, and CBP 5 800 hPa, then the mixing ratios of each cloud which results in the potential to assimilate up to 13 sat- variable are only summed between these two levels. Cloud ellite scans. For this event, 1800 and 2100 UTC retrievals water and cloud ice outside this layer are ignored. Thus, are not available; thus, the final number of satellite the adjustment to model cloud microphysical properties scans assimilated is 11. Defining the minimum number relating to CWP where clouds exist will only occur within of scans to assimilate is based on results from several the vertical bounds of the cloud as defined by the satellite radar data assimilation experiments. These experiments data. If this vertical constraint is not used, then the ob- indicate that approximately 10 radar scans are needed to served CWP would be compared with a CWP value generate an accurate representation of convection for calculated from all model levels. The end result of the a storm-scale analysis (Snyder and Zhang 2003; Xue assimilation would likely be to create a dry bias in the et al. 2006; Caya et al. 2005). One difference between model analysis, since the forward operator implicitly radar and satellite data is that the time interval between assumes the satellite CWP would be spread across all radar observations is generally 5 min or less, resulting in model layers when in fact it should only be spread over a total assimilation window less than an hour. Using a 3-h a limited vertical extent. Assimilating clear-sky (CWP 5 0) assimilation window at convection-allowing scales may retrievals works in much the same way except that the further increase model error over this longer period. vertical extent of the observation remains undefined. Thus, Since variables directly related to the cloud micro- assimilating clear-sky regions should dry the analysis by physical properties are being assimilated, the selection of reducing cloud water and ice mixing ratios if the model the microphysics scheme used in this study is important. generates a cloud. Currently, the GOES-retrieval algo- The cloud properties generated by several schemes in- rithm is not able to resolve multiple cloud layers, rep- cluding Thompson, Morrison, Milbrandt–Yau, and the resenting a possible source of error within the forward National Severe Storms Laboratory (NSSL) two-moment operator. Future algorithm enhancements (e.g., Chang scheme are compared with satellite observations to de- et al. 2010a,b) should better resolve cloud layers and termine which scheme best matched the general observed reduce the impact of this uncertainty. Once the sum- characteristics of thunderstorms (Thompson et al. 2004; mation of model variables is complete, the model Hong and Pan 1996; Milbrandt and Yau 2005; Mansell CWP is compared with the satellite retrieved value

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TABLE 1. Observation types assimilated into each experiment. TABLE 2. Number of available NAv and assimilated NAssim convectional and CWP observations between 1800 and 2100 UTC Expt name Observation type 10 May 2010 for the PATH experiment. METAR: aviation routine CONV Conventional observations only (ASOS, weather report. ACARS, radiosonde) Observation type N N PATH Cloud liquid water path 1 conventional Av Assim Radiosonde_U_wind_component 33 27 Radiosonde_V_wind_component 33 27 Radiosonde_temperature 33 27 and the EnKF makes the appropriate adjustments to the Radiosonde_dewpoint 33 27 model fields to better correspond with observations. The ACARS_U_wind_component 59 58 CWP forward operator described here operates in much ACARS_V_wind_component 59 58 the same way as a total precipitable water (TPW) for- ACARS_temperature 68 66 ACARS_dewpoint 31 30 ward operator recently added to the DART software. METAR_U_10_meter_wind 352 348 c. Assimilation experiments METAR_V_10_meter_wind 352 348 METAR_temperature_2_meter 385 381 Two experiments are conducted to test the viability of METAR_dewpoint_2_meter 377 373 assimilating GOES CWP and the corresponding cloud METAR_altimeter 383 379 GOES_CWP 41 550 41 218 layer information during the assimilation cycle of the Total 43 748 43 367 40-member 3-km ensemble (Table 1). Both experiments use the same 15-km mesoscale background as initial and boundary conditions for the 3-km ensemble. The first time, some form of cloud cover (CWP . 0) exists over experiment only assimilates conventional observations large portions of the domain with 2670 cloudy obser- between 1800 and 2100 UTC and represents the control vations being assimilated, whereas clear-sky (CWP 5 0) experiment (CONV). The second assimilates CWP observations only number 835. Cloudy observations constrained by CTP and CBP in addition to the con- primarily occur in eastern Oklahoma associated with the ventional observations (PATH) over the same 3-h time large low-level cloud field with a second area farther interval. After the final assimilation cycle at 2100 UTC, west ahead of the dryline where developing cumulus forecast fields are output at 5-min intervals out to and ongoing convection is present. In between and 2230 UTC for each member using the hourly mesoscale behind the dryline, no clouds are detected resulting in forecasts as boundary conditions. The impact of assim- CWP 5 0 observations being assimilated. Fewer obser- ilating the satellite data is then ascertained by com- vations are assimilated in southwestern Oklahoma, as paring the ensemble mean analysis and forecast fields they are potentially dust contaminated and thus removed produced from these two experiments. from the observation dataset (Figs. 2b and 4b). b. Observation diagnostics 5. Data assimilation The large number of assimilated CWP observations a. Assimilated observations suggests they should have a large impact on the model Between 1800 and 2100 UTC 10 May 2010, 43 367 analysis fields compared to only assimilating conven- observations are assimilated into WRF-DART for the tional observations. Since the direct assimilation of PATH experiment with GOES CWP retrievals ac- cloud microphysical variables derived from satellite counting for all but 2149 (5%) of that total (Table 2). observations has not been previously undertaken within The remainder originates from conventional observa- a WRF-DART framework, it is important to verify that tions made from the Automated Surface Observing these variables are being successfully assimilated and System (ASOS), Aircraft Communication, Addressing, interpreted correctly. For each 15-min assimilation cycle and Reporting System (ACARS), and radiosonde in- between 1800 and 2100 UTC, the number, bias, root- struments. These are shown in Fig. 4a for the 3-h as- mean-square difference (RMSD), and total spread similation time window. Note the small number of (TSPRD) are computed between the CWP observations observations and observation sites compared to those and the prior and posterior analysis fields. For each cy- available from GOES CWP retrievals, which are shown cle, up to 4000 observations are being assimilated, de- for 2045 UTC in Fig. 4b. Comparing Fig. 4b with the creasing slightly as a function of time as more dust visible imagery in Fig. 1, it is evident that CWP is being contaminated pixels are withheld at later time periods. assimilated in both the clear-sky and cloudy regions Bias between observations and both prior and poste- 2 providing good coverage within this domain. At this rior analysis fields is slightly negative (20.02 kg m 2)

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FIG. 4. Location of observations assimilated into WRF-DART. (a) Conventional surface, aircraft, and radiosonde temperature observations between 1800 and 2100 UTC. Characteristics of humidity, wind, and pressure observations are similar. (b) GOES CWP observations at 2045 UTC with clear-sky (CWP 5 0) and cloudy regions (CWP . 0) denoted. White areas indicate regions where no valid observations exist or are rejected by DART. initially, decreasing to near zero after a few assimilation 700-hPa temperature (T700) and water vapor mixing ratio 2 cycles (Fig. 5). RMSD ranges between 0.1 and 0.7 kg m 2 (Q700) are generated at the initial and final times satellite between 1815 and 2030 UTC with the classic ‘‘sawtooth’’ observations are assimilated (1815 and 2045 UTC). At pattern evident at each time interval (Fig. 5). RMSD 1815 UTC, differences in T700 are confined to the eastern gradually increases out to 2000 UTC before decreasing portions of the domain corresponding to the areas of again for the following two cycles. A jump in both prior thicker stratus clouds (Fig. 6a). A small area of warming and posterior RMSD occurs at 2045 UTC with the prior occurs in north-central Oklahoma coupled with some 2 value exceeding 0.9 kg m 2. The posterior RMSD is less cooling farther east in Arkansas and Missouri. For the than the prior RMSD at each cycle with reductions latter area, Q700 is generally greater indicating that as- 2 ranging from ,0.1kgm 2 early in the assimilation period similating the positive CWP values over this area result in 2 to greater than 0.4 kg m 2 in the case of the final assimi- a cooler, but more moist air mass (Fig. 6b). No significant lation cycle that includes CWP data at 2045 UTC. The differences are apparent in the western portion of the increase in RMSD at 2045 UTC can be partially attrib- domain as deep convection has yet to fire in this region. uted to a significant increase in convection within the do- 2 main after 2030 UTC. The number of high (.1.0 kg m 2) CWP values being assimilated increases nearly 40% from 113 to 152 between 2030 and 2045 UTC. As a result, any errors are likely to be magnified compared to previous 2 assimilation cycle where the number of CWP . 1.0 kg m 2 observations is much lower. Total spread at each cycle closely follows that for RMSD, with values being confined 2 between 0.05 and 0.3 kg m 2 and having the same sawtooth pattern (Fig. 5). The somewhat lower total spread compared to RMSD may indicate that the assumed observation error is slightly too small; however, no filter divergence is observed during the assimilation. This was determined by examining the error curves of several model variables, none of which showed rapid increases in error during the assimilation cycle. The relationship between CWP calculated from model analyzed cloud microphysical variables and observed FIG. 5. Observation diagnostics for CWP showing model bias (circles), RMSD (squares), and TSPRD (triangles) between the CWP is expected to be high as shown above. However, prior and posterior analysis fields for each 15-min cycle starting at the effect on other variables not directly related to cloud 1800 UTC and continuing until 2100 UTC. The number of obser- microphysics is less clear. Thus, analysis increments of vations assimilated during each cycle is also shown (diamonds).

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FIG. 6. Analysis increments for (a) 700-hPa temperature (T700) and (b) water vapor mixing ratio (Q700) at 1815 UTC, which represents the ﬁrst assimilation cycle satellite data are assimilated. (c),(d) As in (a),(b), but at 2045 UTC representing the ﬁnal time satellite data are assimilated.

The location of greatest temperature and water vapor remain present in the northeastern corner of the domain mixing ratio differences shifts toward central Oklahoma in southwestern Missouri in the more stratiform region. associated with the area of developing convection (Figs. The statistical evidence strongly indicates that assimilat- 6c,d). The largest differences in T700 (62.5 K) occur in ing the CWP observations correctly adjusts the analysis southern Kansas near the location of the ongoing con- fields related to CWP to better fit the observations during vection where positive CWP values are being assimilated. each cycle. The following section describes how the ad- Over much of this area, the posterior analysis is cooler justed fields compare with identical fields generated from than the preassimilation analysis. Smaller differences are only assimilating conventional observations. apparent southwestward along the line of developing convection (Fig. 6c). The largest differences in Q700 6. Satellite versus WRF CWP correspond with the location of the temperature differ- a. 1945 UTC ences, though moistening or drying as a result of the assimilation does not always match the cooling or warming To visualize the effects of assimilating GOES CWP in the temperature fields (Fig. 6d). Other differences retrievals into WRF-DART, the ensemble mean CWP

Unauthenticated | Downloaded 10/02/21 09:35 AM UTC 2282 MONTHLY WEATHER REVIEW VOLUME 141 produced by the CONV and PATH experiments is magnitude of these differences is generally smaller than compared with each other and the satellite retrievals. At present farther west in the convective region of the do- 1945 UTC, which represents the eighth assimilation main. Assimilating CWP in the PATH experiment clearly cycle, GOES CWP retrievals show evidence of devel- produces a more physically realistic representation of oping convection along the border of Oklahoma and CWP than present in CONV midway through the as- 2 Kansas near 36.88N, 98.58W where CWP . 1.0kgm 2 similation cycle, which indicates further improvements (Fig. 7a). Additional deep clouds are present farther should be evident by the final assimilation cycle. north and west. In the remainder of central and southern 2 b. 2045 UTC Oklahoma and into north Texas, CWP is ,0.05 kg m 2 indicating mostly clear conditions. Modest CWP values The final cycle where GOES CWP observations are 2 (0.3–0.5 kg m 2) exist over southeastern Kansas, north- being assimilated occurs at 2045 UTC since no satellite eastern Oklahoma, and northwestern Louisiana associ- data are available for 2100 UTC. By this time, the con- ated with the low-level stratus. A stark contrast exists vection along and north of the Oklahoma–Kansas bor- between the observed CWP and that generated from the der has increased significantly in coverage and intensity CONV experiment at the same time (Fig. 7b). The pos- compared to an hour earlier at 1945 UTC (Figs. 7a,d). terior ensemble mean CWP from CONV is often too The largest cell is centered near 37.08N, 97.58W, with 2 aggressive at developing convection ahead of the dryline additional clouds and precipitation (CWP . 1.0 kg m 2) 2 as evidenced by an area of CWP . 1kgm 2 extending farther north. Initial development ahead of the dryline from the Oklahoma–Kansas border south into central into Oklahoma is also beginning to occur by this time. and southern Oklahoma. Farther eastward in Arkansas, Similar to results from 1945 UTC, CONV is too aggres- the analysis CWP is also greater in the stratiform cloud sive in developing and sustaining convection along the regions as well. The effect of assimilating CWP obser- dryline compared to observations (Fig. 7e). In Kansas, vations is apparent as CWP from PATH is significantly where convection is ongoing, its placement in the CONV reduced in central and southern Oklahoma (Fig. 7c). model analysis is wrong, being too far west. Over this area, CWP 5 0 observations are being assimi- In the PATH analysis at 2045 UTC, the magnitude lated, which causes WRF-DART to adjust its cloud water and coverage of CWP is much lower along the line of and cloud ice mixing ratios downward to generate developing convection, which is in much better agree- a matching CWP. The magnitude of the decrease is evi- ment with the satellite observations (Fig. 7f). The maxima dent in Fig. 8a, which displays the difference in CWP in CWP in central and southern Oklahoma are displaced between the two experiments (PATH 2 CONV) at this slightly westward of their location in CONV, indicating time. A large area of negative values with magnitudes that PATH might be somewhat slower in its progression 2 exceeding 0.25 kg m 2 is present in western Oklahoma of its convective features. Figure 8b shows this decrease where PATH suppresses convective development com- as a large area in central Oklahoma where CWP from the pared to CONV. PATH experiment is less than CWP from the CONV 2 Farther north, the coverage and spatial orientation experiment by more than 0.25 kg m 2. Overall, PATH is of the convection near the Kansas–Oklahoma border much less aggressive in developing convection during the (378N, 998W) is well captured by PATH but not by analysis period as it is suppressed at each assimilation CONV. PATH is even able to generate convection cycle. The decrease in analyzed CWP compared to where none exists in the CONV analysis northwest of CONVisadirectresultofassimilatingzerovaluesof the primary cell, matching well with the observations. CWP in this experiment. Additional experiments as- 2 Here, CWP values exceeding 1.0 kg m 2 are being assimilating either zero or positive values of CWP (but similated where the CONV model analysis generates no not both) support this conclusion. When assimilating significant cloud cover. The corresponding adjustment only zero values, the modeled convection is properly in WRF-DART is to increase the cloud water and cloud suppressed, but little change is apparent where con- ice concentrations to produce a CWP that is consistent vection is ongoing. Similarly, assimilating only positive with the satellite observations. The magnitude of this values of CWP results in analyzed CWP values similar 2 increase exceeds 0.25 kg m 2 northof378N where CONV to the CONV experiment in central and southern does not generate convection or displaces it too far east Oklahoma, while only providing modest improvement (Fig. 8a). where convection was ongoing (not shown). Similar Farther eastward in the stratiform cloud regions, results were found by Vukicevic et al. (2004, 2006), CWP values generated by PATH are generally smaller Polkinghorne et al. (2010), and Polkinghorne and than for CONV in the northeastern portion of the domain Vukicevic (2011) when assimilating cloudy radiances and somewhat larger in the southeastern. However, the on similar horizontal scales. Assimilating CWP in clear-sky

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FIG. 7. (a) GOES-13 CWP retrievals at 1945 UTC with corresponding posterior ensemble mean CWP generated from (b) CONV and (c) PATH model analyses at the same time. (d),(e),(f) As in (a),(b),(c), but for 2045 UTC. Black 2 contours in (b),(c),(e),(f) represent 0.5 kg m 2 contour of GOES CWP shown in (a) and (c), respectively.

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FIG. 8. Difference (PATH 2 CONV) in CWP at (a) 1945 and (b) 2045 UTC analysis times after satellite data have been assimilated. Blue regions indicate where PATH generates lower values of CWP whereas red regions indicate that PATH generates higher CWP values. regions is also comparable to assimilating clear-air radar thickness over a particular location. In clear-sky envi- reflectivity (Tong and Xue 2005; Aksoy et al. 2009). ronments, the maximum possible SWF reaches the Both provide the model information that no clouds are surface, whereas increasing cloud cover decreases this present in certain areas and act to suppress spurious amount. If the WRF ensemble is characterizing clouds precipitation that may be generated by the model during correctly, then SWF from Mesonet observations and the assimilation window. the model analysis should be in good agreement. Where In Kansas, assimilating CWP observations produces a there are large differences, it signals that either the model more accurate depiction of the ongoing convection near generates a cloud that does not exist, or the model fails to 37.08N, 97.58W from the PATH experiment compared generate a cloud that does exist. Mesonet SWF obser- to the CONV (Fig. 7f). Where the satellite observations vations indicate extensive cloud cover in northern Okla- 2 indicate convection, CWP generated from PATH in- homa with SWF values generally ,400 W m 2 (Fig. 8). 2 creases by more than 0.25 kg m 2 compared to CONV. Some sites in northern Oklahoma appear almost com- 2 In addition, CWP from PATH is lower farther west pletely cloud covered with SWF , 200 W m 2 (Table 3). where CONV is too aggressive with its convection in In the southeastern portion of the domain, SWF varies Kansas compared to the observations at this time. Thus, from site to site, which is representative of the wavelike assimilating the satellite observations provides both cloud features present over this region (Fig. 1). In the a more accurate depiction of the ongoing convection central and far western portions of the domain, SWF 2 while at the same time suppressing spurious convection. values generally exceed 700 W m 2 indicating mostly Additional differences exist in the eastern portion of the clear skies (Fig. 9a). The exception occurs in a north– domain with PATH generally having somewhat higher south line near the western edge of the domain boundary CWP values than CONV. However, these differences corresponding to the cloud field where convection is de- 2 are generally smaller than in the more convective re- veloping and SWF ranges from 590 to 134 W m 2 (Table 3). gions of the domain. Comparing the SWF observations with values from the CONV ensemble mean reveals several interesting differences (Fig. 9b). The most obvious is the low SWF 7. Verification against Mesonet data values from southwest into south-central Oklahoma 2 (SWF , 400 W m 2) associated with spurious model- a. Downward shortwave flux at 2100 UTC generated convection (Fig. 7). CONV is also somewhat The availability of high spatial and temporal resolution too far west with the convection in far northern Okla- 2 downwelling shortwave flux (SWF) measurements from homa near 988W where Mesonet SWF is .750 W m 2 the Oklahoma Mesonet allows for an independent and corresponding to near-clear-sky values compared to 2 quantitative verification of the differences in cloud CONV values below 250 W m 2 (Table 3). Smaller dif- properties between each model experiment. SWF can ferences exist over the eastern portion of the domain be interpreted as a measure of cloud coverage and where CONV appears to again underestimate SWF in

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22 TABLE 3. Oklahoma Mesonet SWF (W m ) at selected sites along with corresponding CONV and PATH ensemble mean SWF at 2100 UTC.

Site Lat (8N) Lon (8W) Mesonet CONV PATH ACME 34.806 98.006 720.0 409.2 846.5 APAC 34.914 98.292 758.0 454.4 845.9 BIXB 35.963 95.866 414.0 267.8 254.6 BREC 36.412 97.694 160.0 97.4 93.1 BRIS 35.781 96.354 413.0 249.0 330.4 BURB 36.634 96.811 150.0 75.5 208.5 CHER 36.748 98.363 590.0 806.7 746.5 CLRM 36.321 95.646 292.0 340.0 264.2 ELRE 35.548 98.036 762.0 257.6 750.3 FAIR 36.264 98.498 558.0 721.7 508.4 FTCB 35.149 98.467 134.0 400.4 271.4 GUTH 35.849 97.480 677.0 154.6 528.7 HASK 35.748 95.640 393.0 165.7 281.5 MEDI 34.729 98.567 751.0 278.6 784.6 MINC 35.272 97.956 786.0 222.3 735.9 MRSH 36.070 97.360 237.0 110.3 465.7 NOWA 36.744 95.608 126.0 272.3 215.6 OKMU 35.581 95.915 426.0 289.3 309.7 PAWN 36.361 96.770 105.0 88.9 300.6 PORT 35.826 95.560 309.0 229.7 290.8 PRYO 36.369 95.272 270.0 387.3 294.7 WATO 35.842 98.526 399.0 754.2 688.1

east-central regions, but overestimate SWF farther in the northeast. The magnitude of these differences is gener- 2 ally on the order of 100 W m 2 (Table 3). Differences also exist in southeastern Oklahoma, though no consistent pattern of under or over analyzing of SWF is apparent. Calculating the difference between Mesonet and ensemble mean CONV SWF results in a bias (CONV 2 2 Mesonet) of 248.7 W m 2 for the 75 sites in Fig. 9 with 2 a corresponding RMSD of 240.7 W m 2. The negative bias indicates that CONV is underpredicting SWF compared to ground truth observations. The low bias in SWF is a result of CONV developing convection in western Oklahoma too quickly, generating more and thicker clouds, which is consistent with the CWP plot from CONV shown in Fig. 7b. Assimilating CWP results in large changes to SWF with PATH generating higher SWF values over several regions as compared to CONV (Fig. 9c). In the southwestern portion of the domain, PATH generates a large 2 area of SWF . 750 W m 2 in a region where SWF from CONV is several hundred watts per meters squared lower. Mesonet stations in this region all indicate clear or mostly clear conditions and report high SWF values. The PATH FIG. 9. (a) Contour plot of Oklahoma Mesonet SWF at 2100 UTC with corresponding Mesonet locations and wind barbs overlaid 2 2 analysis is in much better agreement with these Mesonet (white) with short barbs 5 5ms 1 and long barbs 5 10 m s 1. observations compared to CONV, with increases in SWF Posterior ensemble mean SWF generated from (b) CONV and (c) 2 by up to 500 W m 2 at many locations (Table 3). Farther PATH at 2100 UTC with Mesonet values overlaid along with model north, CONV has already developed strong convection wind barbs (black). The color contrast between Mesonet and model 2 resulting in a large area of SWF , 200 W m 2. In PATH, SWF indicates where differences between the two are the greatest.

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2 from CONV ranges between 50 and 80 W m 2 higher than the corresponding RMSD from PATH (Fig. 10a). Slightly larger differences are apparent in the bias where 2 CONV is more than 50 W m 2 to low with PATH 2 ranging between 0 and 40 W m 2 too high. Once the assimilation of CWP ends and the free forecast begins, RMSD for both CONV and PATH increases rapidly in the first 15 min. This is a result of the rapid increase in deep convection (high CWP values) in both the model forecasts and observations. The difference in RMSD between CONV and PATH decreases as a function of time becoming negligible 30 min into the forecast at 2130 UTC. This indicates that the increase in accuracy of the SWF fields imparted by assimilating satellite CWP retrievals decreases once the assimilation stops. Similar results have been observed during radar reflectivity assimilation where its effect on the model-simulated reflectivity quickly decreases as a function of time (Snyder and Zhang 2003). Following 2130 UTC, the differences between CONV and PATH are small and continue to decrease as the cirrus shield from the ongoing convection increases in size, uniformly reducing SWF over the eastern half of the domain. The bias is roughly constant between 2100 and 2130 UTC with PATH having the 2 lower absolute value by approximately 10 W m 2. After 2130 UTC, biases for CONV and PATH slowly con- verge to zero at 2230 UTC. For most of this period the absolute value of the bias is lower for PATH as com- FIG. 10. Time series of bias and RMSD for (a) SWF and (b) 2-m pared to CONV indicating that the former continues to temperature between 2030 and 2230 UTC calculated between Mesonet observations and each experiment’s analysis at each time generate less and/or thinner cloud cover over much of step. the domain. While the improvement in SWF RMSD appears to become small 30 min into the forecast, its impact on the spatial extent of the convection and resulting cloud temperature remains for a much longer period of time cover decreases, but some remains. Mesonet observations (Fig. 10b). Changes in temperature fields have a direct of SWF are highly variable in this region, which is a sign of impact on convective instability parameters; thus, they the nonuniform cloud field present. PATH appears to have important implications to downstream convective capture this variability more realistically though the skill development and evolution. The RMSD between each at a few sites may be reduced owing in part to placement experiment and Mesonet 2-m temperature shows that errors. Overall, however, PATH produces much better PATH consistently has a lower RMSD compared to agreement with the Mesonet SWF observations with an CONV. Starting at 2030 UTC and continuing into the 2 RMSD 5 165.5 W m 2 and a smaller, but now positive, free forecast period, the difference is approximately 2 bias of 39.0 W m 2. 0.5 K and slowly decreases to 0.2 K at 2230 UTC. Bias differs by a similar magnitude with PATH being ap- b. Time series proximately 0.5 K warmer for the entire period. Thus, it The improvement in the ensemble mean analyses can be said that PATH is consistently warmer at the from assimilating CWP is not limited to a single time at surface compared to CONV, which is in better agree- 2100 UTC nor is it only evident in the SWF fields. ment with the observations. The primary reason for the Comparisons during both the data assimilation and warmer temperatures is the greater amount of solar forecast periods for SWF and 2-m temperature show radiation reaching the surface early in the assimilation that the assimilation of CWP leads to better agreement cycle. Recall that CONV is too aggressive at devel- with observations (Fig. 10). During the final three as- oping convection in Oklahoma, leading to a reduction similation cycles between 2030 and 2100 UTC, RMSD in SWF reaching the surface, thereby reducing surface

Unauthenticated | Downloaded 10/02/21 09:35 AM UTC JULY 2013 J O N E S E T A L . 2287 temperature. The response in the temperature fields as new techniques are added to the analyses. For example, from changes in SWF is not instantaneous, but rather multilayered cloud retrievals are becoming operational accumulates over time. Thus, temperature differences for optically thin cirrus clouds over low-level water clouds are likely to remain apparent for a longer period of (Chang et al. 2010a,b). Methods for increasing the range time compared to a more rapidly changing parameter of retrieved CWP at night are also being explored (Minnis such as SWF. The reduction in temperature bias and et al. 2012) that will allow for more reliable assimilation RMSD over the entire 90-min forecast period strongly over the entire diurnal cycle. Techniques for estimating indicates assimilating CWP has a lasting impact on the the cloud water content profiles based on the cloud tem- ensemble forecasts. perature and CWP are also being evaluated (Smith et al. 2010). Yet, several important questions remain to be an- swered before these data can be assimilated in real time. 8. Conclusions These questions include analyzing the sensitivity of cloud With the upcoming launch of GOES-R and its Ad- microphysical schemes on assimilating CWP, determining vanced Baseline Imager (ABI), retrieved cloud prop- the best localization and error variance characteristics to erties are scheduled to become available after 2015 use, and improving the representation of multiple cloud (http://www.goes-r.gov/resources/faqs.html) at spatial layers in the forward operator (e.g., Polkinghorne et al. and temporal resolutions comparable to that available 2010). Finally, the relative advantages and disadvantages from operational radar data (Schmit et al. 2005). Fur- of assimilating satellite retrievals along with other high- thermore, the increased spectral resolution of the GOES-R resolution datasets such as radar reflectivity and velocity ABI allows for more accurate retrievals that will act to must also be ascertained. The eventual goal is to maximize reduce potential uncertainties resulting from some of the the use of both satellite and radar observations to gener- assumptions made when creating the CWP forward op- ate an accurate convective-scale model analysis and im- erator for the current generation GOES retrievals. Yet proved very short-range forecasts of convection. despite these uncertainties, the results from this study strongly indicate that the assimilation of CWP is having Acknowledgments. We appreciate the two anonymous a positive impact on the ensemble mean analyses and reviewers whose comments improved the quality of forecasts. During the assimilation period, the PATH ex- this work. Weather Surveillance Radar-1988 Doppler periment consistently produces a better representation of (WSR-88D) level 2 radar reflectivity were retrieved the CWP field compared to the conventional data-only from National Climatic Data Center archives. Mesonet experiment. These results are consistent with previous data were kindly provided by the Oklahoma Climato- research by Vukicevic et al. (2004, 2006), Polkinghorne logical Survey. Nancy Collins at the University Corpo- et al. (2010), and Polkinghorne and Vukicevic (2011) who ration for Atmospheric Research provided invaluable also showed that assimilating high-resolution GOES assistance in helping debug certain parts of the new satellite observations improved the representation of CWP forward operator. This research was supported by cloud properties within a model analysis. the NOAA/National Environmental Satellite, Data, and The improved characterization of clouds and convec- Information Service. Partial funding for this research tion found in this research produces a more accurate SWF was also provided by NOAA/Office of Oceanic and analysis by the end of the assimilation at 2100 UTC when Atmospheric Research under NOAA–University of verified against Mesonet observations. This improvement Oklahoma Cooperative Agreement NA17RJ1227, un- decreases during the forecast period as the effects of as- der the U.S. Department of Commerce. P. Minnis and similating specific cloud characteristics decreases as fore- R. Palikonda are supported by the NASA Modeling, cast time increases. However, the impact of assimilating Analysis, and Prediction (MAP) Program, the GOES-R CWP on downstream surface temperature remains evi- Program, and by the Department of Energy Atmospheric dent 90 min into the forecast as the lower amount of Science Research Program under Interagency Agree- convection generated in the PATH experiment early on ment DE-SC0000991/003. (The near-real-time satellite leads to more solar radiation reaching the surface and analyses can be accessed for a variety of domains at http:// increasing the 2-m temperature. These warmer tempera- angler.larc.nasa.gov/.) tures remain even after both experiments have generated large areas of convection and associated cirrus outflow. REFERENCES Given these positive results, further investigation of Aksoy, A., D. Dowell, and C. Snyder, 2009: A multicase compar- assimilating GOES satellite retrievals is underway. ative assessment of the ensemble Kalman filter for assimila- GOES VISST retrievals are available in near–real time tion of radar observations. Part I: Storm-scale analyses. Mon. (Minnis et al. 2008a) and are continually being upgraded Wea. Rev., 137, 1805–1824.

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