Merging TEMPEST Microwave and GOES-16 Geostationary 1 IR soundings for improved water vapor profiles

Chia-Pang Kuo1, Christian Kummerow1

This paper presents the retrieval of moisture profiles from TEMPEST with the help from 3 ABI water vapor channels with the optimal estimation method. Simulation studies as well as real data demonstrations were carried out. The simulation studies show that adding the 3 ABI water vapor channels benefits the moisture retrievals in both clear and cloudy skies. For real data demonstration, the validation with limited RAOB shows mixed results, which should be expected since real data is a lot more complicated than simulated data. The paper is well organized. Most of the sessions are well written. However, some critical points are not well explained, and some discussions are not convincing. Major revisions are recommended.

Major comments:

1. Why did authors only choose the 3 ABI water vapor channels? The window and CO2 channels, especially the 12um has low level moisture information that should be able to improve the low level moisture retrievals especially in clear sky.
2. The authors didn’t explain how the collocation between TEMPEST and ABI is done. Ideally, this should be done by considering the footprint size of both TEMPTEST and ABI. Since they have different viewing angle, the collocation will result in variable number of ABI pixels within a TEMPEST footprint. A collocation like this also allows using clear ABI pixels to help the retrievals of a partially cloudy TEMPEST footprint.
3. Please add explanation why the auxiliary data is from ERA5, not from others. The ERA5 can actually be better used as another source for evaluation or validation of the moisture retrievals. This is less ideal than RAOB but will give you more samples to work with to gain statistically significant results. For the purpose of weather prediction, using NWP forecast instead of reanalysis data as auxiliary data is also more meaningful.
4. For Table 1, please explain why you designed those configurations. For clear sky, why would you want to set CF to 1? Why don’t you just perform clear sky retrievals? Why did you add CF=1 or ABI CF for clear sky samples? In table 1, for the cloudy sky, explain why you designed those configurations and what you were trying to achieve. Minor question: are you using ABI cloud mask to define clear vs cloudy sky?
5. Table 2 as well, why did you design those configurations and what the differences between them? The numbers are already shown in figure 10. Does “No” mean no ABI cloud products available or you chose not to use? If “yes” means abi cloud products available, why set CF as 0? This is consistent with Figure 10 captain but not figure itself, where CF is set as G16.
6. Explain the configurations in Table 3 as well. The numbers in Table 3 are already in Figure 12.
7. It looks like you use CRTM for both forward simulation and Jacobian calculation for ABI channels. For CRTM, you will need to input the profiles of different cloud species water content and particle size. I assume you can get such information from ERA5 somehow, although not all information is available from ERA5. But CRTM provides Jacobians of the cloud profiles. This would be different from figure 3 which shows Jacobians of clouds are located at 900 and 300 hPa. This makes sense if you are assuming single layer of clouds at those pressure levels. So how did you handle the cloud Jacobians in the retrieval? Did you just convert the Jacobian profiles of clouds to single layer Jacobians? If so, how? Also in Figure 3, I would suggest to show moisture/cloud Jacobians as dTb/dlog(q or clouds). At 300 hPa, it doesn’t make sense to perturb the moisture profile by 1 g/kg. With your current units, it is hard to say the cloud Jacobians show strong signals of clouds. In addition, it might be a good idea to show both moisture and cloud Jacobians at the cloud pressure on the figure. Currently, you are only showing cloud Jacobians at 300 and 900 hPa. Suggest to add two rows either at the top or bottom of the figure for the two cloud layers and leave the original two rows for moisture Jacobians.
8. Discussion of figure 10 from line 478 to 494 is confusing. These are clear sky profiles (assuming you use ABI cloud mask). Why would you set CF=1 for figure 10 a and b? Line 488-490, what do you mean by “forced to be cloud free”? These are categorized as clear profiles based on ABI cloud mask (I am assuming). Unless you don’t trust ABI cloud mask, they should be clear profiles and no clouds are needed in the retrieval. Line 490- 494, what did you use as a priori? Figure 2 shows clear profile below 900 hpa is wetter than cloudy, not consistent with figure 10 e and f. Again, this is clear profiles, why would you suggest to add clouds in the retrievals? What is the bias from ERA5 information in Figure 10f under clear sky assumptions? As compared to truth? What do you mean bias is “even larger”? Since bias could be positive or negative, suggest using more dry biased or wetter biased to indicate the bias change direction. Did you use different a priori for different samples, as shown in Figure 10 e and f? Figure 2 indicates you use the same climatology a priori for all samples.
9. Discussion of figure 12 is also not very convincing. Line 510-511, improvement of moisture retrievals? Which one compared with which one? If between TEMPEST+ABI and TEMPEST only, yes, there is some improvement, not a lot and not consistently of all levels above 800 hPa. If between a and c or b and d, I see even smaller changes. And I don’t know which one is expected to be improved because you never explained the differences and purposes of these configurations. Line 517, how do you know it is correct cloud fraction? Line 519 to 523, how do you know the cloud fraction is too small? The cloud fraction is small. But that doesn’t necessarily mean it is too small. Line 530-531, not all levels above 700 hPa.

Specific comments:

1. Line 70, IASI naming wrong
2. Lines 83-86, NUCAPS has been extended to Metop as well
3. Lines 152-155, these absorption channels are sensitive to both moisture and temperature. We can’t say one is more sensitive than the other. You didn’t need to worry about temperature because you are assuming temperature is known from ERA5.
4. Lines 179-182, please clarify if you are using the same xa for both clear and cloudy profiles.
5. Line 192, is available from GOES-16
6. Line 207, please specify these NEDT at what BT
7. Line 300, This figure will be better shown if you zoom into a few TEMPEST footprints and show how ABI is collocated with it. One panel for BT and the other for cloud mask.
8. Line 321, this is just one sample. You can only calculate the mean bias and STD of whole profile. There is no such thing as mid tropospheric biases and STD. Same is true for line 324-325.
9. Line 338, bias +/- standard deviations of the whole profile.
10. Line 344, why did you only choose 1000 not all available samples. More samples are more statistically meaningful. Same as line 364.
11. Line 367, it is not true that ABI has less sensitivity in lower atmosphere. It all depends on the moisture and cloud condition. ABI has more sensitivity to upper troposphere in wet condition and less sensitivity to lower troposphere in dry condition. Clouds overall reduce moisture sensitivity. But it’s hard to see that from Figure 3 because you didn’t show Jacobians in clear sky for comparison.
12. Line 375-378, authors hinted that the clear profiles are drier than cloudy, which is true for most levels but not below 900 hPa according to Figure 2. Using same a priori for both clear and cloudy profiles would mean the first guess is dry bias below 900 hPa. That is probably the reason why Figure 6 sees more bias below 900 hPa.
13. Line 390, why only choose 8000 samples not all samples?
14. Line 404-406, did you mean IR has less information about ice water content than MW?
15. Figure 10 e and f, why is clear sky drier than cloudy in lower troposphere while figure 3 shows the opposite. Aren’t you using the same a priori for all retrievals?
16. Line 482-482, same argument should be applicable to simulation studies.
17. line 595, improvements between with and without 3 ABI water vapor channels? Line 596, what do you mean by “when ABI is used to identify the scene as cloud free”?

The manuscript demonstrates the synergistic use of cubesat sounding technology with existing instruments from the program of record to obtain science quality water vapor soundings. This paper makes a timely contribution to the decision making process concerned with future sensor acquisition strategies. 

I recommend the paper is accepted, pending minor revisions as indicated below.

1) Using ERA5 for ancillary data comes with some error. The authors should try to add a study to evaluate the impact of the temporal and spatial mismatch on product performance. This can be particularly important when it comes to cloudy scenes. 

2) The authors could draw some expected conclusion from the use of hyperspectral IR geostationary instruments. Of particular importance would be the use of more water vapor channels and the use of hyperspectral IR retrieved temperature and ancillary products in replacement of ERA5.