1. Introduction
Many precipitation retrieval algorithms have been successfully developed for several passive microwave sensors, including Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMIS) (Spencer et al. 1989; Liu and Curry 1992; Petty 1994; Ferraro and Marks 1995; McCollum and Ferraro 2003; Sanò et al. 2013; You et al. 2015), Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) (Kummerow et al. 2001; Viltard et al. 2006; Wang et al. 2009; Aonashi et al. 2009; Gopalan et al. 2010; Petty and Li 2013; Islam et al. 2015; Ebtehaj et al. 2015), Advanced Microwave Sounding Unit (AMSU) and Microwave Humidity Sounder (MHS) (Staelin and Chen 2000; Grody et al. 2001; Chen and Staelin 2003; Weng et al. 2003; Ferraro et al. 2005; Noh et al. 2006; Surussavadee and Staelin 2008; Laviola and Levizzani 2011; Surussavadee and Staelin 2010; Sanò et al. 2015), Advanced Technology Microwave Sounder (ATMS) (Surussavadee and Staelin 2010; Boukabara et al. 2013; You et al. 2016a), and Advanced Microwave Scanning Radiometer 2 (AMSR-2) (Meyers and Ferraro 2016). In addition to algorithms developed specifically for a certain sensor, there are several more generic algorithms, which are applicable to multiple sensors (Chen and Staelin 2003; Shige et al. 2009; Boukabara et al. 2011; Kummerow et al. 2015; Kidd et al. 2016).
These algorithms differ in the following three aspects. First, a variety of statistical approaches link the brightness temperature (TB) with the precipitation rate, including regression (Ferraro and Marks 1995; Wang et al. 2009; McCollum and Ferraro 2003), Bayes’ theorem (Kummerow et al. 2001; Sanò et al. 2013; You et al. 2015), neural network (Sanò et al. 2015; Islam et al. 2015), and shrunken locally linear embedding method (Ebtehaj et al. 2015). Second, the historical precipitation datasets required are derived from several sources, including spaceborne radar [TRMM precipitation radar, Global Precipitation Measurement (GPM) dual frequency precipitation radar, and CloudSat profiling radar] (Wang et al. 2009; Kummerow et al. 2015; Surussavadee and Staelin 2010), ground radar networks (You et al. 2015), or cloud-resolving model output (Kidd et al. 2016). Similarly, the required precipitation profile information can be derived either from cloud-resolving model simulation (Boukabara et al. 2011; Kidd et al. 2016) or from precipitation radar observation (Kummerow et al. 2011). Third, radiative transfer simulations are often indispensable for the more generic algorithms since they need to derive the relationships between TB and precipitation rate for multiple sensors, which often have different channels (Shige et al. 2009; Boukabara et al. 2011; Kummerow et al. 2015). In contrast, radiative transfer models are not necessarily needed when the retrieval algorithm is only for one specific sensor.
These precipitation retrieval algorithms over land seemingly are very different. However, they all share one common feature: using the instantaneous TB in the retrieval process. The primary signature is the TB depression at high-frequency channels (e.g., 85, 166 GHz) due to ice scattering.
To augment existing retrieval algorithms, this study proposes to use TB temporal variation, which is derived from eight polar-orbiting satellites (more details in section 2). It is agreed that the primary precipitation signal over land is the TB depression at high-frequency channels caused by ice scattering. The first motivation of using TB temporal variation is to account for differences in TB starting values that lead to differences in the TB depression by season. For example, corresponding to the same surface rain rate (e.g., 1 mm h−1), the TB at 89 GHz can decrease 10 K from 300 to 290 K in the summer season, while it also can decrease 10 K from 280 to 270 K in the winter season. When TB is directly used in the retrieval process for these two situations, it will result in a large retrieval error unless ancillary temperature information is incorporated in the retrieval process. We will demonstrate that using TB temporal variation, instead of the instantaneous TB, can largely mitigate this issue. Physically, under moderate to heavy precipitation, the high-frequency channels (
To account for environmental temperature variation, several algorithms incorporate temperature information from reanalysis datasets in the retrieval process (Sanò et al. 2013; You et al. 2015; Kummerow et al. 2015). It is shown that incorporating temperature information improves the precipitation retrieval performance. We will demonstrate that TB temporal variation automatically accounts for the environmental temperature variation, without using the ancillary temperature information.
Another common and serious issue in the precipitation retrieval algorithm development is the cold land surface contamination (e.g., snow-covered land), which is particularly problematic for rainfall/snowfall retrieval in winter because the cold land surface naturally possesses a signal similar to the precipitation signal (You et al. 2015; Chen et al. 2016). For example, snow-covered land pixels are frequently misidentified as precipitating pixels, therefore resulting in a large falsely retrieved precipitation rate. It is possible to screen out these snow-covered land pixels using daily snow-cover maps (Helfrich et al. 2007). However, we show later that there still exist some obvious snow-covered pixels even after screening based on daily snow-cover maps. More importantly, in the winter season, snow accumulation on the ground is prevalent. Screening out these pixels will also discard precipitating pixels, leading to many missing precipitating pixels. We will demonstrate that even if the snow-covered pixel is misidentified as a precipitating pixel, the retrieved precipitation rate by TB temporal variation is close to 0 because that TB temporal variation is close to 0.
The objective of this study is to present a new idea for enhancing precipitation retrievals by using TB temporal variation. We will explain where, when, and why TB temporal variation overcomes some of the limitations of the instantaneous TB for precipitation retrievals. This study is organized as follows. Section 2 describes the passive microwave observations from eight polar-orbiting satellites and the precipitation rate from the ground radar observations. Section 3 shows how to convert TBs from other sensors to GPM Microwave Imager (GMI) frequencies by using several statistical methods, including the simultaneous conical overpass (SCO) technique and principal component analysis (PCA). Section 4 presents the major results from this study. Conclusions and future work are discussed in section 5.
2. Data
This study uses the microwave radiometer observations from eight polar-orbiting satellites, including GMI on board the GPM Core Observatory satellite, SSMIS on board Defense Meteorological Satellite Program (DMSP) F17 and F18 satellites, ATMS on board Suomi National Polar-Orbiting Partnership (SNPP) satellite, and MHS on board NOAA-18, NOAA-19, MetOp-A, and MetOp-B satellites. We used all high-frequency channels (
Characteristics of each sensor used in this study. The sensors that employed the cross-track scanning scheme are indicated with an asterisk. For the cross-track scanning sensors, the polarization (V/H) is valid only at nadir. The ascending ECT (local time) is as of December 2016 for the sun-synchronous orbit satellites. The GPM satellite has a precessing orbit, which means that it overpasses a certain location at varying times throughout the day.
Table 1 also shows the ascending equatorial crossing time (ECT) as of December 2016 for the sun-synchronous orbit satellites. The descending ECT is 12 h earlier than its ascending counterpart. The GPM satellite has a precessing orbit, which means that it overpasses a certain location at varying times throughout the day. Approximately, there is at least one observation in about 3 h for a certain location from these eight satellites’ observations. That is, the daily revisit frequency is at least eight times for a certain location over the equatorial region. We show later that over the targeted region, the daily revisit frequency varies from 10 to 16 times, because of the increasing overlap in adjacent swaths as the satellite flies poleward.
All these channels have different footprint resolutions (Draper et al. 2015). The slightly different frequencies among them (e.g., 89.0 GHz from GMI versus 91.7 GHz from SSMIS) also result in different TBs for the same observations (Yang et al. 2014). In section 3, we demonstrate a method to bring all these frequencies to a similar resolution. We also convert the TBs from SSMIS, ATMS, and MHS to GMI frequencies by the SCO technique (Yang et al. 2011) and PCA method (details in section 3).
The reference precipitation rate data are from the Multi-Radar Multi-Sensor (MRMS) system, which is at 1-km and 2-min spatial and temporal resolution (Zhang et al. 2016). Collocation between the MRMS precipitation rate and TB is discussed in section 3. Previous work demonstrated that the MRMS precipitation rate is less accurate in mountainous regions due to terrain blockage and in the cold season due to shallow cloud systems (Chen et al. 2013; Tang et al. 2014). A radar quality index (RQI) is developed to represent the MRMS precipitation data quality (Zhang et al. 2011). This study only uses the precipitation data with RQI greater than 0.5. This threshold value (0.5) is chosen by considering the trade-off between the sample size and the quality of radar precipitation estimates.
The National Ice Center’s Interactive Multisensor Snow and Ice Mapping System (IMS) daily snow-cover map at 24 km resolution (Helfrich et al. 2007) is used to determine whether a pixel is associated with snow cover on the ground. This study does not distinguish snow-covered land from ice-covered land. We use “snow-covered land” purely for convenience, which includes both snow-covered land and ice-covered land. It is also worth mentioning that the “frost” phenomenon may contribute to false precipitation detection from satellite observations. However, the temporal resolution from these eight satellites (Table 1) is about 3 h. Considering the shorter frost life cycle, these satellite observations probably cannot account for the frost effect.
Data used in this study are all from March 2014 to December 2016 over the land portion of 25°–50°N, 130°–60°W. We choose this period of time since observations from all aforementioned eight satellites are available.
3. Methodology
This section first describes a method to bring all channels from all sensors to a nominal resolution. Then we discuss how to use the SCO technique (Yang et al. 2011) to obtain the pair of pixels between GMI and the other seven sensors, where the GMI is taken as the reference. Based on the SCO pairs, we show how to use the PCA approach to convert TBs from the other seven sensors to GMI channels. Further, we define TB temporal variation. The linear discriminant analysis (LDA) approach for precipitation screening is discussed. Finally, we show how to define the “same location” observations from these eight polar-orbiting satellites.
a. Aggregate the higher resolution TB datasets
The mean footprint resolution of GMI, SSMIS, ATMS, and MHS for the frequencies used in this study is listed in Table 1 (Draper et al. 2015). The GMI has the highest footprint resolution with 7 km at 89.0 GHz and 6 km for higher frequencies (166 and 183.3 GHz). The SSMIS mean footprint resolution is 14 km. The footprint resolution from ATMS and MHS varied from 14 to 45 km from nadir to edge and from 17 to 45 km from nadir to edge, respectively. This study took the SSMIS mean footprint resolution (14 km) as the nominal resolution. The higher footprint resolution from GMI is aggregated to match this resolution, by simply averaging the closest 4 GMI pixels at 89.0 GHz (14 × 14/7/7 = 4) and 6 GMI pixels at 166 and 183.3 GHz (14 × 14/6/6 ≈ 6). For ATMS and MHS, we keep their original footprint size. The footprint size of ATMS and MHS at nadir is similar to the nominal resolution. However, the footprint size over the edge is significantly larger. We consider the varying footprint size from the center scan lines and the edge scan lines when converting the TBs to GMI channels in the next section.
For the precipitation rate, we simply average the closest 196 (14 × 14 = 196) 1-km MRMS precipitation rate pixels for each TB observation at the closest time.
Better collocation schemes (e.g., weighted average and Backus–Gilbert method) may further improve the result presented in this study. However, these schemes are much more time consuming than the simple average currently employed in this study. Considering the amount of data from eight satellites, we choose to utilize the simplest scheme as a proof of concept.
b. Convert TBs from other sensors to TBs at GMI frequencies
After the footprint sizes of these eight sensors are brought to a similar resolution, we convert TBs from the other seven sensors to TBs at GMI channels. The GMI channels are taken as the reference channel because SSMIS, ATMS, and MHS are calibrated against GMI (Berg et al. 2016). From Table 1, it is clear that all other sensors have similar frequencies with those at GMI. The channel similarity between GMI and the other seven sensors enables us to convert TBs from other sensors to TBs at GMI frequencies.
It is worth mentioning that the 150 GHz channel of SSMIS (F18) has stopped functioning since February 2012. Therefore, for SSMIS (F18), the 150 GHz channel is not used in the TB conversion process. Considering the high correlation between the 150 and 91.7 GHz channels, and between the 150 and 183.3 GHz channels, the absence of the 150 GHz channel likely does not significantly affect the estimated TBs at GMI frequencies.
In the following discussion, we take the GMI and SSMIS (F17) as an example to discuss the conversion process. SSMIS (F17) frequencies are 91.7 (V/H), 150 (H), 183.3
For the sounders (ATMS and MHS), previous work showed that the TBs from edge and center scan lines differ (Weng et al. 2003; Yang et al. 2013; You et al. 2016a). To consider the scanning position effect, for ATMS we group the SCO pairs based on the scan line position into three categories. Specifically, we group the SCO pairs between GMI and ATMS into left-edge SCO pairs (scan position from 1 to 32), center SCO pairs (scan position from 33 to 64), and right-edge SCO pairs (scan position from 65 to 96). Similarly, the SCO pairs between GMI and MHS are grouped into left-edge (1–30), center (31–60), and right-edge (61–90) SCO pairs. Ideally, one would group the SCO pairs to 96 and 90 categories for ATMS and MHS, which fully considered the scanning position effect. However, because of the limited sample size for each scan position, we only group them into three categories. After separating the center and edge SOC pairs, similar procedures between GMI and SSMIS are applied. That is, for each SCO pair (left edge, center, and right edge), we derive different regression coefficients to converts the TBs into TBs at GMI channels.
1) SCO technique
The basic assumption of the SCO technique is that simultaneous measurements at a location from two different sensors at a similar frequency should be highly correlated. This study takes the GMI observations as the reference. Two satellite measurements, one from GMI and the other one from any of the other seven sensors, are called an SCO pair, if the overpass location is less than 1 km and the overpass time is less than 5 min. These threshold values (1 km and 5 min) are chosen by considering the trade-off between the sample size and the SCO pair accuracy.
Over the targeted region from March 2014 to December 2016, there are 39 529 SCO pairs between GMI and SSMIS (F17), 37 285 SCO pairs between GMI and SSMIS (F18), 16 401 SCO pairs between GMI and ATMS, 12 773 SCO pairs between GMI and MHS (NOAA-18), 12 979 SCO pairs between GMI and MHS (NOAA-19), 14 011 SCO pairs between GMI and MHS (MetOp-A), and 11 576 SCO pairs between GMI and MHS (MetOp-B). As discussed in the previous section, the SCO pairs between GMI and each MHS, and between GMI and ATMS, are equally split into three categories based on scan positions.
2) PCA
In this section, we use SCO pairs between GMI and SSMIS (F17) as an example to explain the TB conversion process. The same procedure is applied to SSMIS (F18). For ATMS and each MHS, this procedure is applied to the three subcategories based on the scan positions.
Each of the 39 529 SCO pairs between GMI and SSMIS (F17) is associated with six GMI TBs [89.0 (V/H), 166 (V/H), 183.3
For SCO pairs between GMI and SSMIS (F17), we first decomposed the GMI TBs (six channels) into six PCs (denoted by 
The coefficients from 
A similar procedure is applied to the other six sensors. By doing so, it is as if we have eight sensors measuring TBs at GMI frequencies, which are 89.0 (V/H) 166.0 (V/H), 183.3
c. Definition of TB temporal variation
We would like to emphasize that 
Clearly, in this definition, we did not consider the environmental variation (e.g., the temperature and water vapor) from 
d. LDA
We choose the DI threshold value for precipitating or nonprecipitating situations, corresponding to the false alarm rate (FAR) at 0.10. Choosing other DI threshold values, corresponding to different FAR values (e.g., 0.05 or 0.15) will only change numerical values in this study. However, the conclusions hold. Previous work showed that including large-scale environmental parameters (e.g., vertical velocity and relative humidity) can improve the precipitation detection performance (You et al. 2015; Behrangi et al. 2015). As a proof-of-concept work, we do not include these parameters in the current study.
e. Definition of the “same location”
The objective of this study is to demonstrate that 
4. Results
a. Two cases of TB time series
This section shows TB time series over two locations. In each case, we first show time series for H89, which is the most sensitive channel to the surface characteristics among the channels used in this study. As a comparison, time series for V190 are also shown, which is less sensitive to surface features and more sensitive to hydrometeors in the air.
Figure 1a shows the time series of H89 from March 2014 to December 2016 over the grid box at 43.5°N, 74°W in New York. In Figs. 1b–h, TB at H89 is estimated from ATMS, MHS (NOAA-18), MHS (NOAA-19), MHS (MetOp-A), MHS (MetOp-B), SSMIS (F17), and SSMIS (F18), respectively. The sample number from each sensor at this location is also shown in Fig. 1 (e.g., N = 1097 from GMI in Fig. 1a).
Time series of H89 from March 2014 to December 2016 over the grid box at 43.5°N, 74°W in New York: (a) observed from GMI and estimated from (b) ATMS, (c) MHS (NOAA-18), (d) MHS (NOAA-19), (e) MHS (MetOp-A), (f) MHS (MetOp-B), (g) SSMIS (F17), and (h) SSMIS (F18).
Citation: Journal of Hydrometeorology 18, 9; 10.1175/JHM-D-17-0050.1
First, it is clear that H89 from these eight sensors have similar seasonal variation. The dynamical range also is similar. The cold TBs in the winter season of 2015/16 (December–February) are obvious from each sensor. The daily snow-cover map shows that the majority of these pixels are associated with snow-covered land. These pixels are frequently misidentified as precipitation pixels, which leads to large false precipitation estimation. We show later that using TB temporal variation can largely mitigate the snow-covered land contamination. The time series from each sensor are not identical because each sensor overpasses this location at different times.
Second, using all these observations from eight sensors significantly increases the revisit frequency for this location, which is essential to calculate TB temporal variation. We demonstrate later that the shorter the revisit time, the better the correlation between TB temporal variation and precipitation intensity is, which is especially the case over the rapidly changing land surfaces (e.g., snow-covered land).
The time series of V190 in the same period of time at the same location are also analyzed. As expected, V190 has a much smaller seasonal variation (figure not shown because of space limitations), compared with that at H89 (Fig. 1a), because it is less affected by the surface characteristics than H89. On the other hand, similar to the H89, V190 from different sensors behaves very similarly. Figures 2a and 2b show the combined time series of H89 and V190 at this location, respectively. There are no obvious outliers observed when pooling data from all eight sensors together. It indicates that our method can effectively convert TBs from other sensors to GMI channels. Similar characteristics are noticed from other channels (V89, V166, H166, and V186).
(a) Time series of H89 from March 2014 to December 2016 over the grid box at 43.5°N, 74°W in New York, from all sensors. (b) As in (a), but for V190. (c) As in (a), but over the grid box at 30.5°N, 86°W in Florida. (d) As in (b), but over the grid box at 30.5°N, 86°W in Florida.
Citation: Journal of Hydrometeorology 18, 9; 10.1175/JHM-D-17-0050.1
Another case over the grid box at 30.5°N, 86°W in Florida also is demonstrated in Figs. 2c and 2d. At this location, the seasonal variation is much less pronounced for both H89 and V190. In particular, V190 has no noticeable seasonal variation (Fig. 2d). Again, there are no obvious outliers observed in Figs. 2c and 2d, indicating that our method effectively converted TBs from other sensors to GMI channels. In the next several sections, we show that TB temporal variation in this location can significantly alleviate environmental variations and can therefore lead to a better correlation between precipitation intensity and TB temporal variation, compared with the instantaneous TB.
It is worth mentioning that long spikes (i.e., cold TBs) in Fig. 2 generally correspond to the precipitation occurrence. However, the snow-covered land also can lead to cold TBs (e.g., the spikes in January and February over the grid box at 43.5°N, 74°W in New York in Fig. 2a). These pixels often are falsely identified as precipitation pixels. We show later that TB temporal variation is almost insensitive to the contamination from these snow-covered pixels.
To summarize, this section demonstrates that the SCO and PCA approaches can effectively convert TBs from other sensors to GMI channels.
b. Correlation between TB temporal variation and precipitation intensity
Figure 3 shows the correlation coefficients of precipitation intensity with the instantaneous TB (V89, H89, …, V190) and 
(left) Correlation between the instantaneous TB and precipitation rate. (right) Correlation between precipitation rate and 
Citation: Journal of Hydrometeorology 18, 9; 10.1175/JHM-D-17-0050.1
The superiority of 
(a) Scatterplot based on correlation between 
Citation: Journal of Hydrometeorology 18, 9; 10.1175/JHM-D-17-0050.1
For the rest grid boxes (about 8.0%), 
1) Snow-covered land effect
This section uses the data from the previously mentioned grid box at 43.5°N, 74°W in New York to explain why 
As shown previously, this location frequently experiences snow accumulation over the ground in the winter season. The correlation between 
Case study over the grid box at 43.5°N, 74°W in New York. (a) Scatterplot between precipitation rate and 
Citation: Journal of Hydrometeorology 18, 9; 10.1175/JHM-D-17-0050.1
Observations can be further divided into non-snow-covered data and snow-covered data. For the non-snow-covered data, the correlation between 
For pixels over snow-covered land, the correlation between 
The red, green, and magenta curves in Figs. 5a–f are regression lines derived from the least squares approach. Figure 5g shows that the regression curves from the entire dataset (red line), non-snow-covered subset (green line), and snow-covered subset (magenta line) are almost identical, which essentially means that the relationship between 
The relative independence of 
We further analyzed the snow-covered land contamination at V190 (Fig. 6). Similarly, 
As in Fig. 5, but for V190.
Citation: Journal of Hydrometeorology 18, 9; 10.1175/JHM-D-17-0050.1
2) Environmental variation effect
This section focuses on data from the grid box at 30.5°N, 86°W in Florida to explain why even in a rarely snow-covered region, 
To demonstrate the effects of environmental (e.g., temperature, humidity) variation, we analyze the relationships between precipitation rate and 
Case study over the grid box at 30.5°N, 86°W in Florida. (a) Scatterplot between precipitation rate and 
Citation: Journal of Hydrometeorology 18, 9; 10.1175/JHM-D-17-0050.1
As stated in the introduction, the problem is that the starting values from which H89 decrease are different in summer and winter. In the summer season, H89 decreased from about 282 K (green curve in Fig. 7d), as opposed to 268 K in winter (magenta curve in Fig. 7f). However, 
In summary, this section shows that 
c. Correlation seasonal variation
This section analyzes the seasonal variation of the correlation between TBs themselves and precipitation rate, and between 
In spring, the largest correlation improvement is observed over the Rocky Mountain region and areas north of 45°N. This improvement is more obvious for V89 and H89. Similar features are observed in fall. In summer, the correlation improves very little by using 
The largest improvement is observed in the winter season, when the snow accumulation on the ground is prevalent. In this situation, 
Figure 8a shows that the correlation between H89 and precipitation rate is 0.15. It is worth mentioning that almost all the pixels in this location in winter are associated with snow accumulation on the ground, as determined by the IMS daily snow-cover map. The positive correlation is clearly caused by the misidentified snow-covered pixels, which are associated with no precipitation. Using 
Case study over the grid box at 47°N, 114°W at Missoula, Montana. (a) Scatterplot between precipitation rate and H89. (b) Scatterplot between precipitation rate and 
Citation: Journal of Hydrometeorology 18, 9; 10.1175/JHM-D-17-0050.1
d. Time difference influence
The objective of this study is to show 
Table 2 shows the observation number from each sensor from March 2014 to December 2016 in the targeted region. GMI has the smallest sample size with 19.01 million observations, due to the relatively narrow swath coverage. For the other seven sensors, each has about 30 million observations. On average, the revisit frequency for any sensor is less than 2 times daily. By combining observations from all eight sensors, the revisit frequency is greatly improved. The revisit frequency is improved to 10–16 times daily, depending on the latitude (Fig. 9). A much more frequent revisit for a certain location leads to a much shorter 
Sample size of each sensor from March 2014 to December 2016 at 0.25° resolution in the targeted region (25°–50°N, 130°–60°W).
Daily revisit frequency from eight sensors for each 0.25° grid box based on observations from March 2014 to December 2016.
Citation: Journal of Hydrometeorology 18, 9; 10.1175/JHM-D-17-0050.1
Figure 10 shows the histogram of the time difference (i.e., 
(a) Histogram of the time difference [
Citation: Journal of Hydrometeorology 18, 9; 10.1175/JHM-D-17-0050.1
To show the variable 
(a) Correlation between 
Citation: Journal of Hydrometeorology 18, 9; 10.1175/JHM-D-17-0050.1
Over the Southeast region, the correlation is almost independent from the 
In a postprocessing mode, it is possible to find the closest nonprecipitating scene by checking the succeeding observations. By doing so, it can further shorten 
To summarize, this section demonstrates that observations from these eight satellites significantly increase the revisit frequency, which is crucial for effectively exploiting the signature of 
e. One sensor versus eight sensors
It is found in the previous section that 
The first column of Fig. 12 shows the correlation between precipitation rate and GMI TB for its six channels. In the second column, we show the correlation between precipitation rate and 
(left) Correlation between the instantaneous TB and precipitation rate, using GMI observation only. (center) Correlation between precipitation rate and 
Citation: Journal of Hydrometeorology 18, 9; 10.1175/JHM-D-17-0050.1
Next, we compute the correlation between precipitation rate and 
In summary, it is demonstrated that 
f. Retrieval performance
Previous sections have demonstrated that precipitation rate is more highly correlated with 
As mentioned previously, there are several sensors with the highest possible frequency at ~89 GHz (e.g., AMSU-A and AMSR-2). Therefore, we first apply this simple linear regression algorithm to V89 only, and then TBs at all frequencies are used to retrieve the precipitation rate.
1) Retrieval results from V89 only
Figure 13 shows the simple single-channel retrieval performance over the entire region and the Northeast (37°–47°N, 65°–80°W) and Southeast (30°–35°N, 80°–90°W) regions. The retrieval over the entire region based on 
Precipitation retrieval performance in 2016 by using V89 and 
Citation: Journal of Hydrometeorology 18, 9; 10.1175/JHM-D-17-0050.1
Using V89 itself, the simple regression retrieval performance is very poor over the Northeast region with the correlation of 0.33, RMSE of 1.45 mm h−1, and bias of −59.41% (Fig. 13c). However, these statistics are significantly improved from 
In the Southeast region, the improvement is not as large as that over the Northeast region (cf. Figs. 13e and 13f). However, we indeed notice that there are large improvements in the lower end of the precipitation intensity distribution from 0.2 to 2 mm h−1. In this range, the 
In summary, this section shows that the simple single-channel regression retrieval that results from 
2) Retrieval results from all channels
This section builds on the previous section and applies a multichannel regression retrieval to demonstrate the value of 
Figures 14 and 15 show the geospatial distribution of the retrieved precipitation rate from each of the eight sensors. Each row of Fig. 14 and Fig. 15 shows the MRMS observed precipitation, the retrieved precipitation from all TBs (V89, H89, …, V190) for each sensor, and the retrieved precipitation from all 
Case study of the blizzard case over the U.S. Mid-Atlantic and Northeast on 23 Jan 2016. Each row shows the MRMS observed precipitation, the retrieved precipitation from TBs themselves for each sensor, and the retrieved precipitation from 
Citation: Journal of Hydrometeorology 18, 9; 10.1175/JHM-D-17-0050.1
As in Fig. 14, but for sensors of MHS (MetOp-A), MHS (MetOp-B), SSMIS (F17), and SSMIS (F18), respectively.
Citation: Journal of Hydrometeorology 18, 9; 10.1175/JHM-D-17-0050.1
For GMI, it is noted that the retrieval results from 
For ATMS (second row of Fig. 14), retrieval results from both TB (Fig. 14e) and 
The value of the 
Scatterplots between MRMS precipitation rate and retrieved precipitation rate from all eight sensors based on all TBs, and between MRMS precipitation rate and retrieved precipitation rate from all eight sensors based on all 
Citation: Journal of Hydrometeorology 18, 9; 10.1175/JHM-D-17-0050.1
The correlation, RMSE, and bias for each sensor from this event are listed in Table 3. Better statistics from 
Correlation, RMSE, and bias based on TB and 
Marked improvement also has been found for multichannel regression retrieval performance based on 
Next, the retrieval performance is assessed over the whole region and the Northeast and Southeast regions. Figures 17a and 17b show the overall retrieval results from TBs and 
Precipitation retrieval performance in 2016 by using all TBs (V89, …, V190) and all 
Citation: Journal of Hydrometeorology 18, 9; 10.1175/JHM-D-17-0050.1
Seasonal retrieval performance is also evaluated. Figures are not shown because of space limitations. Retrieval results from 
5. Conclusions and discussions
This study proposes a new approach to improve precipitation rate retrievals over land: using TB temporal variation 
We first developed a method to convert TBs from other sensors to GMI channels. Time series analysis shows no obvious bias from this conversion. By doing so, the observation frequency is significantly increased. Specifically, the revisit frequency for any single sensor in the targeted region is less than 2 times daily. By combining all the observations from these eight sensors, the revisit frequency is increased to 10–16 times daily, depending on the latitude. Further analysis shows that the much more frequent revisits for a certain location are crucial to obtain stronger correlation between 
We demonstrate that 
Further analysis shows that the correlation between 
A simple single-channel regression precipitation retrieval proof of concept shows that by only using 
Analysis from a 2016 blizzard case over the United Sates demonstrates that the major limitation of using TB directly is the overestimation at the low intensity end of the precipitation rate distribution, where surface contamination plays a larger role. Finally, it is shown that a multichannel regression retrieval based on all 
One key step of this study is to identify the precipitation status for each observation, which directly affects the 
Not only does this study highlight the importance of maintaining the current microwave constellation, it also implies that a geostationary microwave radiometer can significantly improve the precipitation retrieval over frequently snow-covered regions, by capitalizing on the surface and atmosphere “background” information contained in TB temporal variation.
Future work seeks to 1) extend this work to the GPM-covered land regions (65°S–65°N), through incorporation of 
All satellite data are downloaded from NASA Precipitation Processing System (PPS) website (https://storm.pps.eosdis.nasa.gov/storm/). MRMS precipitation data are downloaded from the National Centers for Environmental Prediction (NCEP; http://mrms.ncep.noaa.gov/data/). We thank Dr. Wesley Berg for the information on SSMIS status. Comments by Dr. Joseph Munchak were very helpful in improving the original manuscript. The Equatorial Crossing Time (ECT) is provided by Dr. Eric Nelkin. This work is supported by NASA’s Precipitation Measurement Missions Program science team via solicitation NNH15ZDA001N-PMM. Dr. Song Yang also would like to acknowledge the financial support from NRL base project “River Influence at Multi-scales (PE 61153N).” The authors would like to acknowledge the support from colleagues in the PMM Land Surface Working Group (LSWG).
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