Table 1.
The OSSE short names, full names and motivating questions.
[i] The OSSEs are described in detail the text and Table 2. Note that except for Control and 1Polar the full names include the added polar orbit names in parentheses. The polar platforms for Control are MetOp-A , MetOp-B , and SNPP and for 1Polar these are MetOp-A and MetOp-B .
Table 2.
LEO observing system configurations for the OSSEs.
[i] All other observations are the same across all OSSEs and are described in the text. The motivations for the different OSSEs are given in Table 1. The first several rows of this table give the platform name or names for each sensor (rows) that are used in each of the experiments (columns). MetOp indicates both MetOp-A and MetOp-B platforms, an equal sign indicates the same as Control and an ‘X’ indicates the sensor is not used in that OSSE. There are two shifted simulated platforms: F18’ , which is F18 shifted by 30° eastward and TRMM’ , which is TRMM shifted by 180° eastward. All the platforms are in polar orbits except that TRMM and TRMM’ are in tropical orbits. The following rows compare the experiments in terms of the number of sensors that are operating in the IR or MW bands and in early morning, late morning, afternoon, and tropical orbits. In these rows the values for Control are given and then the change relative to Control for the other experiments. An equal sign indicates no change from Control, while the zero for AM MW indicates one sensor was added (EON-MW) and one sensor was removed (ATMS). The last row gives the number of distinct orbits in use in each OSSE. For this purpose, MetOp-B and MetOp-A are considered to be in the same orbit since they follow each other in approximately the same orbital plane.
Table 3.
EON-MW sensor characteristics compared to those of ATMS for each channel (i).
[i] EON-MW and ATMS are compared for central frequency (GHz), bandwidth (GHz for a single side band), and polarization. Values for ATMS are from https://www.star.nesdis.noaa.gov/mirs/snppatms.php and values for EON-MW are from W. Blackwell (pers. comm., October, 2017). For clarity, we define GHz, GHz, and GHz. Note that ATMS polarizations are either quasi-vertical (QV) or quasi-horizontal (QH). Differences with respect to ATMS are highlighted in bold.
Fig. 1.
BT difference statistics for EON-MW minus ATMS for each channel for bias, RMSE, minimum and maximum (different line types and colors), calculated for a diverse set of 58 cases that are used to test CRTM. The test cases, obtained from a GSI analysis are for both land and ocean, and clear and cloudy conditions.
Fig. 2.
Weighting functions (unitless) calculated for EON-MW (solid lines) and ATMS (dotted lines) for (a) the surface and temperature sounding channels and (b) the water vapor sounding channels, both for a standard atmosphere. Note the different vertical axes.
Table 4.
ATMS and EON-MW error standard deviations.
[i] The columns are for the channel number (i), the specified temperature sensitivity (NEΔT, K) for EON-MW, the specified and pre-launch measured NEΔT for ATMS, and the estimated observation errors (EOE) for ATMS used during the DA in the OSSEs. Values for NEΔT for ATMS from Kim et al. (2014) and for EON-MW from W. Blackwell (pers. comm., October, 2017).
Fig. 3.
Six-hour data coverage for simulated EON-MW observations including (a) the F18 early morning orbit, (b) the afternoon SNPP orbit, (c) the high obliquity TRMM (tropical) orbit, and (d) the shifted F18 ( F18’ ) orbit. The six-hour windows are centered on 0000 UTC 8 August 2006 (a, b, d) or 0600 UTC 8 August 2006 (c).
Fig. 4.
Vertical profiles of retrieval mean errors (bias, a, c) and RMSE (b, d) for temperature (K, a, b) and water vapor mixing ratio (%, c, d) for four MW sensors (colors) for the 8 August 2006 independent sample described in the text.
Fig. 5.
SAMs for AMSU-A/MHS, SSMIS, EON-MW, and ATMS for temperature (left) and water vapor mixing ratio (right) for the same sample as Fig. 4. In this case, the PAMs include RMSE, correlation, and bias of temperature and water vapor mixing ratio profiles. The color bars are for ECDF normalization and the black outlines are for rescaled minmax normalization (Hoffman et al., 2018). Confidence intervals for the ECDF SAMs are plotted at the 95% level and grey shading indicates the 95% confidence interval for the null hypothesis (H0) that there is no difference between sensors.
Fig. 6.
Geophysical capability assessment for different geophysical variables (columns) and key sensor attributes (rows) for (top) EON-MW, (middle) all SNPP sensors (ATMS, CrIS, VIIRS), and (bottom) SNPP sensors complemented by the sensors on two additional polar orbiters—AMSU-A/MHS and HIRS/4 on NOAA-18 and AMSU-A/MHS, IASI, and HIRS/4 on MetOp . Among the attributes, performance refers to error size, density to the observation spacing, and reliability to impact of losing one sensor in a constellation. The color in each cell indicates the capability assessment for that geophysical variable (column) and attribute (row). Green, yellow, and red indicate optimal, marginal, and low capability, respectively.
Fig. 7.
Flow chart of the OSSE system. Here, blue are inputs, orange are processes, and green are calculated quantities, although calculated quantities such as the error added simulated observations are also the input to the next process. See text for explanation.
Fig. 8.
DA diagnostics for BT globally averaged over two weeks for (top, a–d) ATMS from an impact experiment using real observations with the same set up as experiment ATMS but for 2014 and for (bottom, e–i) EON-MW from the PM experiment. For each of ATMS and EON-MW, the following statistics are given as a function of channel: (a, e) global mean of O–A and O–B (K), (b, f) global standard deviation of O–A and O–B (K), (c, g) the bias correction (K) relative to the analysis and background, and (d, i) the number of observations assimilated (counts/day).
Fig. 9.
Vertical profiles of analysis mean error (i.e. bias, a, c) and analysis RMSE (b, d) for geopotential height (m, a, b) and relative humidity (%, c, d) for the different OSSEs (colors) for the global domain.
Fig. 10.
Scorecard comparing forecast skill for anomaly correlations and RMSE for different variables at different levels for different forecast lengths for AM vs. Control. The symbols and colors indicate the probability that AM is better than Control. As shown below the scorecard, the green symbols (from left to right) indicate that AM is better at the 95%, 99% and 99.9% significance levels, respectively, while the red symbols indicate that AM is worse at the 99.9%, 99% and 95% significance levels, respectively. Gray indicates no statistically significant differences and blue indicates that the anomaly correlations in the tropics are not considered.
Fig. 11.
Forecast impacts in terms of (a) global ECDF SAMs and (b) ECDF SAMs as a function of forecast time for each experiment (colors). The color bars are for ECDF normalization (Hoffman et al., 2018). Confidence intervals for the ECDF SAMs are plotted at the 95% level and grey shading indicates the 95% confidence interval for the null hypothesis (H0) that there is no difference between experiments. Correlations have been accounted for in determining the confidence intervals as noted in the text. Note that the forecast time zero SAM in panel (b) is the analysis SAM since verification is with respect to the G5NR.
Fig. 12.
Analysis and forecast impacts in terms of ECDF SAMs by (a) level (hPa), (b) domain, (c) variable, and (d) statistic for each experiment (colors). The color bars are for the analysis SAMs (forecast hour 0 only) and the black outlines are for the forecast SAMs (forecast hours 24 through 168). In this figure, confidence intervals are plotted at the 95% level for the forecast SAMs and grey shading indicates the 95% null hypothesis (H0) confidence interval for the analysis SAMs.