The expected performance of cloud optical and microphysical properties derived from Suomi NPP VIIRS day/night band lunar reflectance

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH: ATMOSPHERES, VOL. 118, 13,230 13,240, doi: /2013jd020478, 2013 The expected performance of cloud optical and microphysical properties derived from Suomi NPP VIIRS day/night band lunar reflectance Andi Walther, 1 Andrew K. Heidinger, 2 and Steven Miller 3 Received 30 June 2013; revised 31 October 2013; accepted 3 November 2013; published 9 December [1] The day/night band channel of the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on board Suomi-National Polar Partnership (S-NPP) is a visible/near-infrared sensor ( nm band pass) capable of measuring extremely low magnitudes of light, down to the levels of reflected moonlight and beyond. Whereas similar measurement capabilities have existed on predecessor sensors (principally, the Defense Meteorological Satellite Program), the day/night band offers the first calibrated radiance measurements, and as a result, it is the first opportunity to apply moonlight measurements to the problem of retrieving nocturnal cloud optical properties. Daytime retrievals of cloud properties such as top height, optical thickness, cloud top particle size, and water content, have been conducted routinely from an assortment of operational and research grade optical sensors for decades. These observations are providing a satellite-based global data record of increasing relevance to climate change monitoring (where clouds are thought to play an integral feedback role). The lack of a complete diurnal record of such key parameters presents an important shortfall of these records. Here we present the adaption of the daytime cloud optical and microphysical properties algorithm, which derives cloud optical thickness and effective radius from reflected sunlight to lunar reflectance. The new algorithm is referred to nighttime lunar cloud optical and microphysical properties. Day/night consistency of optical depth is shown through global analysis for one complete day of VIIRS data. Limitations of the retrieval of effective radius are discussed. Citation: Walther, A., A. K. Heidinger, and S. Miller (2013), The expected performance of cloud optical and microphysical properties derived from Suomi NPP VIIRS day/night band lunar reflectance, J. Geophys. Res. Atmos., 118, 13,230 13,240, doi: /2013jd Introduction [2] Knowledge of cloud microphysical properties is fundamental to the study of the Earth s water and energy budget, aerosol/cloud feedback, and precipitation. The estimation of cloud optical depth (COD) and effective particle radius (REF) using satellite observations of solar reflectance is well established [Nakajima and King, 1990; Roebeling et al., 2006]. The lack of sunlight during night presents a persistent gap in studying the diurnal cycle of cloud properties with satellite images. In particular, the available nighttime measurements (infrared emission based) provide 1 Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin, Madison, Wisconsin, USA. 2 NOAA/NESDIS Center for Satellite Applications and Research, Madison, Wisconsin, USA. 3 Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado, USA. Corresponding author: A. Walther, Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin, 1225 W. Dayton, Madison, WI 53706, USA. (andi.walther@ssec.wisc.edu) American Geophysical Union. All Rights Reserved X/13/ /2013JD only limited sensitivity to COD due to strong absorption [Minnis et al., 1998]. For example, the Geostationary Operational Environmental Satellite-R (GOES-R) Algorithm Working Group IR emission-based cloud algorithm (nighttime lunar cloud optical and microphysical properties (NLCOMP)) provides a maximum COD value of 8. Another limitation of the IR techniques only operates if there is a substantial temperature difference between cloud top and surface. Observations from passive microwave sensors offer sensitivity to the cloud water path, but this comes at the expense of significantly coarser spatial resolution, which results in the significant averaging of subpixel heterogeneity and loss of cloudspecific detail. [3] Certain cloud types show a distinctive diurnal variability. As an example, the liquid water content and the optical depth of marine stratocumulus clouds usually decrease from sunrise to sunset and increase over the night. Closing the nighttime observation gap would benefit a wide range of basic research and operational applications. Examples include a better understanding of the diurnal cycle of cloud processes, implications to the atmospheric/surface radiation budgets with impacts to circulation, and now-casting applications. Retrievals from polar-orbiting satellites could also help immensely in the context of cloud characterization at high 13,230

2 latitudes (e.g., during Arctic winter), where the availability of visible light is limited or nonexisting for extended durations. [4] Contemporary nighttime cloud property retrievals deal with the detection of clouds, the cloud height, and the delineation of cloud types [Heidinger et al., 2012]. While infrared and microwave observations are available at all times of the day, they do not provide the same sensitivity over a wide range of cloud optical thickness values as provided by solar reflectance in visible spectral range. Methods that use measurements in the infrared spectrum make use of the dependency of optical properties to cloud emissivity. These methods are limited to cirrus clouds with optical thickness less than about 5. Some insight of microphysical content of clouds can be gained from satellite-based microwave retrievals of liquid water path, but microwave methods tend to be applicable only to the optically thick water clouds and, as mentioned above, at relatively coarse spatial resolution (tens of kilometers in contrast to ~1 km from optical spectrum radiometers). [5] The daytime cloud optical properties (DCOMP) algorithm retrieves cloud optical depth (COD) and cloud top effective particle radius (REF) for many sensors that share similar bands in the visible/near-ir spectrum [Walther and Heidinger, 2012]. It is based on bispectral (visible and near-infrared) reflectance measurements, which provide joint sensitivity to cloud optical depth and particle size. The retrieval is conducted within an optimal estimation inversion framework [Rodgers, 1976], which enables a full propagation of input and forward model uncertainties to solution uncertainty, including assessment of information content provided by the observations vis-à-vis the a priori constraints associated with the first-guess as well as the nonretrieved components of the forward model which bear influence on the retrieval. [6] During the portion of the lunar cycle when the Moon is above the horizon at a given time of evening, its light is the principal source of visible illumination during nighttime, even though it is about 250,000 times dimmer than the Sun. This is the reason that the sensitivity of conventional visible channels of most of the existing sensors is far too low to quantitatively measure moonlight reflectance. However, if such calibrated radiance measurements were available, they could be used for cloud property retrievals in a way analogous to those derived from sunlight. [7] The Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) satellite has provided observations of low-light visible emission and reflectance (including moonlight) since the early 1970s. The main use of this sensor has been for qualitative nephanalyses and various nonatmospheric applications such as city and population management [Elvidge et al., 1997]. The OLS provides a broadband channel, which is capable to give insight to cloud structure. The sensitivity of this channel is about 4 magnitudes higher than visible channels of other sensors, such as National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA- AVHRR). However, coarse radiometric resolution (6 bit), spatial resolution (~3 5 km), and the uncalibrated nature of these data limit their utility in the context of quantitative cloud property retrievals. [8] The Visible Infrared Imaging Radiometer Suite (VIIRS) on the NASA Suomi-National Polar Partnership (S-NPP) provides a unique new capability relevant to cloud remote sensing. The day/night band (DNB) channel, one of 22 bands featured on VIIRS, offers highly sensitive observations in the visible spectral region ( nm) during day and night. As will be described later, the DNB is well calibrated. Techniques have been developed to generate lunar reflectance from the DNB by way of time-dependent lunar irradiance models coupled to the DNB radiance observations. By recasting radiance into units of reflectance, one can relate the measurements to certain intrinsic properties of the cloud in a way analogous to daytime reflectance measurements. [9] This paper describes the extension of the daytime bispectral technique used for retrieving cloud optical depth and particle size using visible lunar reflectance and near-infrared radiance to the VIIRS data. This algorithm will be referred to as the nighttime lunar cloud optical and microphysical properties (NLCOMP) algorithm. The use of solar reflectance from 0.65 to 3.9 μm, which corresponds to measurements in the VIIRS channels M5 and M12, has been successfully demonstrated for DCOMP and also in several former studies [Platnick and Valero, 1995]. The absence of a reflection component in the 3.9 μm channel observation impacts the performance of the NLCOMP approach significantly relative to its daytime analogs, suggesting that future observing systems designed with low-light capability would benefit from additional bands in the near infrared. This paper demonstrates the initial implementation of NLCOMP and provides a sensitivity study that illustrates in which cloudiness regimes NLCOMP is successful, as well as which regimes challenges exist. [10] The article is structured as follows: The technical details of DNB and the computation of moonlight reflectance are explained in sections 2 and 3. Section 4 introduces the retrieval and its theoretical information content. Section 5 shows the performance for selected case studies. The paper concludes with some examples that further demonstrate key aspects of NLCOMP performance and enumerate certain applications that may benefit from this new capability. 2. VIIRS DNB [11] The Visible Infrared Imaging Radiometer Suite (VIIRS) is an optical-spectrum scanning radiometer on board the Suomi-National Polar-orbiting Partnership (S-NPP) satellite, which was launched on 28 October The satellite flies in a sun-synchronous (0130 local time, descending node) orbit with an orbital altitude of 824 km and an inclination of 98.7, which offers considerable overlap of 3000 km wide swaths at high latitudes. S-NPP is a prototype of the future operational weather satellite constellation the Joint Polar-orbiting Satellite System (JPSS). VIIRS is a Moderate Resolution Imaging Spectroradiometer (MODIS)- like instrument, which measures in visible and infrared spectral range ( μm). It has 16 moderate resolution bands (M-bands) with spatial resolution of about 750 m at nadir, 5 imagery bands (I-bands) with a resolution of 375 m, and the day/night band (DNB), which has a uniform resolution of 742 m over the entire track. The DNB measures visible radiances with a center wavelength of 0.7 μm and a full-width half-maximum response of approximately nm. The sensitivity is at about 4.0e 05 W m 2 sr 1, which is about 4 magnitudes higher than the sensitivity of the visible channels on AVHRR or MODIS. 13,231

3 Figure 1. Variation along a scan line of the pixel size for the VIIRS M-bands (blue) and the VIIRS DNB. [12] The DNB calibration is critically important to the performance of NLCOMP and is described in Goddard Space Flight Center [2012]. A major issue in the radiometric performance of the DNB has been the presence of stray light artifacts. A fix was implemented into the official VIIRS processing stream in August 2013 that largely removed this issue. Future NLCOMP validation studies will attempt to quantify the DNB calibration uncertainty and its impact on the retrieved properties. [13] Another unique feature of the VIIRS DNB is that its pixel size is nearly constant both along and cross track over the entire 3000 km wide swath [Schueler et al., 2013]. Figure 1 illustrates the variation in pixel size along one scan line for a VIIRS M-band and the VIIRS DNB. The pixel sizes cross-track growth behavior of all VIIRS M-bands is the same. The matching of the DNB and M-bands is important to NLCOMP since it uses both types of observations to characterize the cloud properties. Currently, a nearest-neighbor approach is employed that selects the nearest DNB pixel corresponding to an M-band pixel. No attempt is made to average DNB band together when an M-band s footprint encompasses multiple DNB pixels. Also, the swath width of the DNB exceeds that of the M-bands, and the data that fall outside the M-band swath are ignored. The development of improved collocation techniques is a subject of ongoing research. 3. Lunar Reflection Model [14] The model that is used to compute lunar reflectance is developed and described in detail in Miller and Turner [2009]. The main challenge is determining the incoming downwelling irradiance coming from the Moon. During daytime, the solar flux is well known. In contrast, the moonlight flux is a function of a number of very complex components, such as the lunar cycle, lunar zenith, the Sun/Moon/ Earth geometry, and the lunar surface albedo. All these complicating factors of the light path cause a higher variability of Figure 2. Variation of global coverage of calibrated lunar reflectance for 4 days of a waxing lunar cycle that began with new moon on 11 January 2013 and reached a full moon on 27 January. Lunar images as seen from the Northern Hemisphere are provided for visual reference and taken from Areas that are black represent regions where there was insufficient solar or lunar illumination. The global coverage of lunar reflectance is up to 88% for full moon scene. 13,232

4 lunar reflectance in comparison to daytime solar reflectance. The Miller and Turner [2009] model provides a means to compute the time-varying lunar spectral irradiance and convolves this result with the DNB sensor response function to provide an in-band, downwelling top-of-atmosphere irradiance (E M ). The conversion from DNB radiance (L) to equivalent isotropic reflectance (R) follows as: R ¼ πl= ðμ M E M Þ (1) where μ M is the cosine of the lunar zenith angle, information obtained from the DNB auxiliary data present in its geolocation file. The stated uncertainty of the Miller and Turner [2009] model is 7 12% for DNB observations but can be within 5%, with greatest uncertainties at higher (>120 ) and lower (< 5 ) lunar phase angles due to phasedependent variations in the lunar albedo which are not accounted for in the initial model. Work is ongoing to provide a first-order correction to the phase-dependent lunar albedo, and these improvements will be incorporated into future versions of the NLCOMP retrieval. [15] The lunar reflectance models and the improvement of the radiometric performance of the VIIRS DNB continue to develop. The final performance of NLCOMP will of course depend intimately on these outcomes. In addition, there are many other recognized sources of light during the night besides lunar reflection that contributes to the DNB signal. These include predominantly stable and quasi stable light emission from sources such as cities, oil platforms, and natural gas flares and also ephemeral sources such as aurorae wildfires, lightning flashes, and maritime vessels. Even the faint emission and integrated reflectance of light the atmosphere itself (nighttime airglow) are discernible in the VIIRS DNB [Miller et al., 2012], but those signals are sufficiently smaller than lunar reflectance and thus do not pose a significant challenge to the NLCOMP algorithm. [16] It is also important to note that global coverage of the calibrated lunar reflectance varies across the ~29.5 day lunar cycle. This variation is illustrated in Figure 2, which shows global fields of the VIIRS lunar reflectance computing for the descending node data from selected days of January These 4 days show the waxing phase of a lunar cycle that began with a new moon on 11 January and ended with a full moon on 28 January. The small lunar views in Figure 2 show the moon phases on the 4 days selected. This figure shows that near global coverage is achieved for +/ 6 days of the full moon. The overall value for a full cycle is about 40% of the time. Based on the conservative filtering thresholds of the reflectance model (which includes enforcement of astronomical darkness, where the Sun is at least 19 below the horizon, and no twilight contributions from atmosphere scatter are deemed present), there is no lunar reflectance coverage 40% of the time (+/ 6 days of the new moon). Another benefit is the availability of lunar reflectance over the winter pole region (the North Pole in Figure 2), where solar reflectance is absent for a long period. 4. Algorithm Description NLCOMP [17] In this section, we present the theoretical basis of the nighttime retrieval. The daytime retrievals are based on reflectance measurements in a visible (0.6 μm) and in a water-absorbing near-infrared channel (either 1.6, 2.2, or 3.75 μm). The previous section articulated how moonlight as measured by the DNB can be also used as a source of visible reflectance. However, VIIRS does not provide a corresponding highly sensitive channel in the near-infrared region, which could provide lunar reflectance in a spectral region where cloud particles absorb and thus provide particle size information. Many sensors have a channel, which measures, in the near-ir region, around 3.8 μm where the signal is a combination of terrestrial radiation and solar/lunar reflection. Some daytime retrievals use only solar reflectance, and this component of the signal is isolated by subtracting the emitted part from the measured radiation, a technique that has been found to be accurate to within ~5% for optically thick clouds [e.g., Miller, 2001]. DCOMP employs a forward model, which takes in account both sources of radiation. This requires the use of auxiliary information of cloud temperature, surface temperature, surface emissivity, and atmospheric profiles of absorbers, particularly water vapor. Those data may come from numerical weather model output, which have a certain degree of uncertainty. This has also a big impact of DCOMP product quality, depending on the fraction of radiation, which comes from emission. [18] They here introduced nighttime lunar cloud optical and microphysical properties (NLCOMP) retrieval uses lunar reflectance from the DNB together with radiance measurements from VIIRS channel M12 to retrieve COD and REF for nighttime scenes under moonlight illumination conditions. The measured signal in the near-infrared channel consists during night solely on emitted radiation from atmosphere including clouds and surface. This complicates the retrieval because it lacks the information content inherited in bidirectional reflection function. NLCOMP uses the dependency of emission and transmission on the optical properties. In this way, surface and cloud top temperature and their uncertainties have a bigger relative impact of the forward model. In the daytime case, most of the information comes from solar reflection that is not sensitive on the cloud temperature. [19] The method used to generate the DNB reflectance tables is very similar to the tables for solar reflectance described in Walther and Heidinger [2012] and is computed following Heidinger [2003]. Figure 3 shows the cloud transmission (left) and emissivity (right) values computed at 3.75 μm for a water phase (top) and ice phase (bottom) cloud. The phase discrimination is done by a cloud-typing algorithm described in Pavolonis et al. [2005]. Emissivity and transmission are computed separately using a multiple-scattering radiative transfer model [Heidinger, 2003] and are not constrained to sum to unity. Basic assumptions of the forward model are single-layer plane-parallel homogeneous clouds with spherical droplets for liquid clouds and ice clouds, which consist on aggregate column particles. Figure 3 shows that for optically thick clouds (COD > 10), there is only information on REF. For optically thinner clouds (COD < 4), the contours show a slope with components in directions, but optical depth is the dominant sensitivity for thin clouds (COD < 4), and the sensitivity to REF is smaller for ice clouds than that for water clouds. Figure 3 also indicates that the sensitivity to REF is highest for small particle (REF < 20). The patterns shown in these tables will ultimately dictate the retrieval performance. 13,233

5 Figure 3. Theoretical cloud emissivity and transmission as functions of REF and COD for VIIRS channel M12 for a single-layer cloud with a sensor zenith angle of 44. [20] The reflectance forward model for the visible channel is identical to the one used for DCOMP: R toa ¼ R c ðθ 0 ; θ; Δϑ; τ; r e Þ þ At cðθ 0 ; τ; r e Þt c ðθ; τ; r e Þ 1 ASðτ; r e Þ where R c is the bidirectional cloud reflectance function, A the surface albedo, t c the direct cloud transmission function, and S the cloud spherical albedo. The geometrical dependencies are denoted by the source (now lunar as opposed to solar) zenith angle θ 0, the sensor zenith angle θ, and the relative azimuth angle Δϑ. COD and REF are symbolized as τ and r e in all equations of this paper. We assume surface albedo A as known from MODIS white-sky climatology. [21] The forward model for the 3.75 μm (M12) channel radiance is defined as R ¼ ε c ðτ; r e (2) ÞBT ð c Þþt c ðτ; r e ÞR sfc þ R ab (3) where B(T c ) is the Planck function of the cloud top temperature T c, R ab is the emitted clear-sky radiance from above the cloud, and R sfc is from below the cloud including contributions from the surface. Values of ε c and t c are provided from the emissivity/transmission lookup tables as shown in Figure 3 and will be optimized during the retrieval process. The cloud top temperature T c is derived from the Algorithm Working Group Cloud Height Algorithm (ACHA) [Heidinger and Pavolonis, 2009] and developed initially for the Geostationary Operational Environmental Satellite-R (GOES-R) Advanced Baseline Imager sensor but extended to VIIRS. ACHA is an optimal estimation approach that derives cloud top temperature, 11 μm cloud emissivity, and an 11/12 μm microphysical index from the available longwave IR channels. For VIIRS, these are the 8.5, 11, and 12 μm observations. Realistic surface and atmospheric properties are obtained from reanalysis data, taken from the National Centers for Environmental Prediction Climate System Forecast Reanalysis [Saha et al., 2010] and surface emissivity databases [Seemann et al., 2008]. [22] Equations (2) and (3) compose the set of forward model equationsusedinnlcomp.thetaskistofind a pair of [τ, r e ] which satisfies both equations simultaneously. The different input uncertainties and the forward model error have to be considered to find the optimized most likely solution. This is achieved via minimization of a scalar cost function. NLCOMP uses the same inversion approach of an optimal estimation technique as DCOMP does. Details to this inversion approach can be found in Walther and Heidinger [2012]. [23] To evaluate the information content of NLCOMP, equation (3) is simplified by neglecting the usually weak atmospheric absorption in R sfc and R ab. To reasonable approximation, we can recast this equation for channel M12 as R ¼ ε c ðτ; r e ÞBT ð c Þ þ t c ðτ; r e Þε sfc BT ð sfc Þ (4) [24] The measured radiance R is a function of both the radiance emitted from the cloud expressed as the Planck function of cloud top temperature B(T c ) weighted by cloud emissivity ε c and the radiance emitted by the surface B(T sfc ) weighted by cloud transmission t c. The surface emissivity ε sfc is, with the exception of desert land types, mostly close to unity (i.e., blackbody). Cloud transmission and emissivity 13,234

6 Figure 4. The bispectral function of COD and REF as a function of DNB reflectance and M12 brightness temperature for four different examples of cloud top temperature, surface temperature, and surface emissivity. The colored lines depict the isolines of effective radius. The dashed lines and numbers label cloud optical thickness. have the tendency to counterbalance in respect to optical thickness of the cloud as shown in Figure 3. Thin clouds transmit a substantial component of upwelling surface emission, while thicker clouds block the upwelling radiation from below the cloud and provide information through the cloud emissivity function. [25] Considering these extreme cases makes it clear that there is an alternating behavior in the radiance to effective radius dependency. The emissivity increases with effective radius, but transmission decreases. For thick clouds, higher radiance translates to higher effective radius. For thin clouds, this rule is inverted, i.e., higher radiance corresponds to lower effective radii and more scattering clouds, which allow more upwelling surface radiation to transmit through them. We cannot assume information content within the blind spot transition region of COD between these both regimes. [26] An additional complicating factor is that the Planck function is highly nonlinear with temperature. Depending on the absolute temperature values of T sfc and T c and the temperature difference between the two, the blind spot is shifted to either thinner or thicker cloud. In some cases, one single behavior dominates the entire forward model function. To support the discussion, Figures 4 and 5 show examples of the forward model presented as bispectral function images for ice and water clouds. All cases are computed for an example observing zenith angle of 40. We decided to use brightness temperature (BT) for the y axis units instead of radiance, because uncertainty estimates for VIIRS near-infrared M-bands are given as a linear estimate in respect to temperature. The noise equivalent differential temperature for band M12 is given as 0.3 K. Figure 4 (top left) shows a cold ice cloud (cloud top temperature of 220 K) over a cold surface (260 K) with a surface emissivity of Opaque cloud BT behavior is valid for clouds with a visible COD larger than ~15. Figure 3 demonstrated that cloud emissivity ranges from ~0.85 for small particles to ~1.0 for larger particle sizes, consistent with what is found in Figure 4 for this region. Higher emissivity means that the cloud will be emitting at temperature closer to its environmental temperature, which for this cold cloud top leads to a lower observed radiance, and corresponding lower BT in the measurement. An emissivity of 1.0 (blackbody) provides a measurement of the actual cloud top temperature, which can be seen at the green line in Figure 4a. [27] COD is solely a function of the DNB reflectance, a property that manifests in the bispectral diagrams as COD contour lines that are nearly parallel to the y axis. The extremely optically thick clouds cannot be quantified due to signal saturation effects. The shape of graph for optically 13,235

7 Figure 5. Same as Figure 4 but for water clouds. thinner clouds is determined by the transmission function and surface temperature. The spacing of the contour lines is larger for COD between 3 and 10 than for optically thicker clouds, indicative of higher information content in this region. Measurements of very thin clouds (COD <1) do not provide enough information to retrieve both COD and REF. The blind spot regime of the retrieval ranges from COD = 10 to COD = 15. [28] The remaining three examples of Figure 4 consider a relatively warm ice cloud over a warm surface, a desert surface example, and an ice cloud close to freezing point over warm surface. It can be seen from this various examples that the blind spot varies greatly for different conditions. Also, the information content outside the blind spot may be different depending on the influence terms with less importance. As an example, the desert case with a very warm background surface shows very narrow REF isoclines, which makes the retrieval of REF impossible. [29] Water clouds exhibit a similar behavior to ice, since the liquid-phase emissivity and transmission tables are qualitatively similar to those of ice. However, the cloud top temperature is in general higher (warmer) than for ice clouds which leads to a greater contribution of the cloud top temperature and emissivity to the measured signal. The blind spot is usually shifted to smaller COD values, which actually increases the frequency of retrievable water clouds relative to ice clouds. The examples in Figure 5 show that for a considerable range of surface temperature up to 290 K, we can expect the blind spot to occur for COD less than or equal to 8 for most of the cases. Given that most liquid-phase clouds are optically thick, these plots suggest a promising range of utility exists for NLCOMP, particularly for maritime clouds (e.g., represented by Figure 5 (bottom left)). It is interesting that optically thin clouds have a higher sensitivity to COD with the M12-band BT relative to DNB reflectance. While for thick clouds, a single-channel DNB approach would provide COD results with an agreeable quality; thin water clouds require the bispectral approach. [30] To summarize the theoretical information content analysis: We expect COD to be retrievable over the entire range of 1 to 100. REF information content is limited to only regions outside a blind spot of COD values, where cloud transmission and emissivity effects compensate each other. This blind spot is not in a fixed COD range but depends on the cloud and surface temperatures, so dual-variable information will only be available in certain conditions using the current VIIRS DNB/M12 observations. As stated previously, a version of M12 sensitive to the lunar reflectance signal (as opposed to only the thermal emission component) could provide augmented utility. [31] The examples above did not include an account for observation and forward model uncertainties. Here we assume measurement errors of 5% for DNB reflectance and 0.3 K for M-band BT. These error values can be directly 13,236

8 Figure 6. Daily composites of 1 day of VIIRS observations from 27 January (left) Solar reflectance (M5) and DCOMP optical depths from ascending (daytime) observations. (right) Lunar reflectance (DNB) and NLCOMP optical depths from descending (nighttime) observations. incorporated in the optimal estimation framework and propagated to yield a corresponding uncertainty of the solution. The main sources of forward model uncertainty of NLCOMP were identified as being associated with cloud top temperature, surface temperature, and surface emissivity for the near-infrared (M12) model and surface reflectance for the visible (DNB) model. As discussed in Walther and Heidinger [2012], the retrieval cannot account for errors associated with incorrect assumptions for the cloud phase or for the presence of multilayer cloud levels. 5. Performance of NLCOMP, Case Studies [32] Cloud products derived from satellite observations are inherently difficult to validate, and this becomes especially challenging at night where the availability of solar reflectance based methods is not available. The baseline test of the NLCOMP is ostensible consistent with the traditional solar reflectance methods when considered on a global scale (backing away from the details of diurnal cloud availability). For this analysis, we use the results from the DCOMP approach, which processes S-NPP data through the Pathfinder Atmospheres Extended (PATMOS-x) processing system. [33] Figure 6 shows global images of PATMOS-x level-2b composites of S-NPP data for 27 January Figure 6 (left) shows the solar reflectance (DCOMP) results from the ascending-node (daytime) observations, and Figure 6 (right) shows the lunar reflectance (NLCOMP) results from the previous night s (roughly 12 h earlier) descending-node observations. Figure 6 (top) shows the solar and lunar reflectance images, and Figure 6 (bottom) shows the corresponding optical depth retrievals. One can see from these images the consistency in the reflectance and optical depth images. While the solar reflectance values seem to be higher for bright clouds, the optical depth values seem similar (due in part to a general decrease in sensitivity of reflectance to optical depth at higher COD values, as inferred in Figures 4 and 5). [34] The ostensible consistency between the day and night optical depth retrievals shown in Figure 6 is confirmed quantitatively in Figure 7, which compares the distributions of COD. The data used in the distributions came from pixels, which were cloudy during both observations. This simple filter acts to mitigate any differences in cloud detection sensitivity during day and night. Figure 7 (left) shows the results for 23 January (a gibbous moon at ~0.75 fractional disk illumination), and Figure 7 (right) shows results for 27 January (full moon). Both images show that the day and night optical depth distributions are similar. This represents a major advance of NLCOMP in comparison to the heritage IR-only approaches. In addition, the consistency of the day and night results seems to high for the full moon conditions (27 January) and for the 3/4 moon case (23 January). [35] In order to provide a further check on the veracity of the NLCOMP results, a more detailed check of the day/night 13,237

9 Figure 7. Comparison of the global distributions of cloud optical depth from S-NPP VIIRS derived during day from the DCOMP approach (solid line) and the NLCOMP approach (dashed line). (left) January 23 (3/4 Moon). (right) 27 January (full moon). consistency was performed on a marine stratus scene. Stratus clouds are known to exhibit a consistent diurnal cycle. They tend to dissipate during the day and grow during the evening hours, with a maximum in spatial extent and water mass occurring around sunrise [Wood, 2012]. Given that the DCOMP algorithm also operates on the current NOAA GOES imagers, the daytime algorithm was applied to GOES data for this comparison in order to provide COD estimates that were closer in time to the nighttime S-NPP overpass. Figure 8 shows the evolution of the cloud optical depth over a region dominated by stratus clouds off the coast of California on 26 April The figure shows the transition from early evening (1830 local) to night (~0130) to morning (0930) using GOES and VIIRS data. [36] Similar results from the NASA Langley Research Center GOES-15 cloud products exist on the web at cloudgate2.nasa.larc.gov. These NASA retrievals are IRonly based on the shortwave-infrared infrared split-window technique (SIST) algorithm [Minnis et al., 1998]. These SIST images convey the well-understood challenges posed by IR-only remote sensing of low-level clouds like marine stratus. Their opacity and lack of temperature contrast with the surface limit both detection and property characterizations. The SIST algorithm is regarded in the community as being among the most advanced, well-validated, and broadly utilized algorithm of its type, but even this algorithm struggles to provide the cloud optical depth variation seen in the solar or lunar reflectance approaches. In a qualitative sense, Figure 8. Comparison of the cloud optical depth evolution from VIIRS and GOES-15. All data are from 26 April Image on the right is NOAA/DCOMP applied to the GOES-15 evening observed at 6:30 P. M. local time, the GOES-15 morning observed at 9:30 A.M. local time, and the VIIRS night observed at 1:30 A.M. local time during full moon condition. 13,238

10 Figure 9. Comparison of the cloud optical depth and cloud effective radius distributions from the DCOMP and NLCOMP results shown in Figure 8. Only pixels classified as water phase are included. the NLCOMP results seem to be temporally and spatially consistent with GOES/DCOMP results and with the expected diurnal evolution of this stratus deck as noted previously and in various studies such as Wood [2012]. Figure 9 illustrates this by showing the distributions of COD (left) and REF (right) for the VIIRS and GOES results, recalling from the sensitivity analysis that NLCOMP retrievals of REF are optimized for this particular liquid cloud over water regime. The cloud optical depth modes fall in line with the expected growth pattern in stratus clouds. The diurnal cycle in effective radius is less clear, but these do indicate that the NLCOMP and DCOMP distributions are not grossly inconsistent with each other. 6. Conclusions [37] The Visible Infrared Imaging Radiometer Suite (VIIRS) on the NASA Suomi-National Polar Partnership (S-NPP) provides unique new capabilities relevant to cloud remote sensing. The most novel addition to the passive radiometer observations on VIIRS is without doubt the day/night band (DNB), which offers observations in the visible spectral region during day and night. The DNB is well calibrated, and under sufficient moonlight, techniques have been developed to generate lunar reflectance. With lunar reflectance, techniques can be applied at night and potentially generate cloud properties that are consistent with those from the day. The nighttime lunar cloud optical and microphysical properties (NLCOMP) algorithm has been developed to exploit this capability and derive cloud optical depth and particle size at night using the VIIRS lunar reflectance. Comparison with daytime results from the physically consistent DCOMP approach demonstrates that consistency of NLCOMP and DCOMP under a range of lunar illuminating conditions. This consistency was further demonstrated through a comparison over a stratus region using GOES DCOMP results, which surrounded the NLCOMP observation time. Cloud effective radius results over this stratus region also indicate consistency with the daytime though a global demonstration of effective radius consistency remains to be demonstrated. Sensitivity studies indicate that certain cloudiness regimes may possess blind spots to algorithm and may limit the accuracy of cloud effective radius retrievals from the NLCOMP approach, especially for cold ice clouds. [38] The current version of NLCOMP is limited to nonurban regions, where no static background signals may disturb the retrieval. This is a remaining challenge. As a next upcoming step to a more complete global retrieval, we plan to generate background maps of static light sources. Those will be developed as dynamic maps of assumed clear-sky DNB radiation in a certain preceding time period (1 week to a month). There is a chance that quantitatively adding city light as a source of radiation to the NLCOMP forward model may make it possible to retrieve COD over cities to an of course very limited accuracy. This would extend NLCOMPs possible retrieval options to new moon over urban areas. Though, we can only expect a very limited exactness. [39] Otherwise, NLCOMP is well capable to retrieve marine liquid-phase clouds; those are particularly important for radiation budget studies. We expect here the highest accuracy, because relatively warm clouds are combined with ocean temperature data for which the error is assumed as low. On a long term, NLCOMP can help to extend regional International Satellite Cloud Climatology Project-like cloud climatologies of COD and REF to nighttime statistics. [40] As the present results already show, NLCOMP gives deeper insights in the diurnal variability of cloud properties. Model parameterization of clouds diurnal variability can benefit from these results. This will be even truer when the complete JPSS satellite constellation with more satellites is in space, and observations are made more than once per night. [41] Acknowledgments. This work was funded by the JPSS Program Office Data Products and Algorithms Division (DPA) led by Heather Kilcoyne. The Atmospheric PEATE at the University of Wisconsin SSEC provided much of the data used here. The views, opinions, and findings contained in this report are those of the author(s) and should not be construed as an official National Oceanic and Atmospheric Administration or U.S. Government position, policy, or decision. References Elvidge, C. D., K. E. Baugh, E. A. Kihn, H. W. Kroehl, E. R. Davis, and C. W. Davis (1997), Relation between satellite observed visible-near 13,239

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