Pros and Cons of Polar Orbiting and Geostationary Satellites

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When considering the pursuit of a robust climate record, the strategic use of both types of satellites proves highly beneficial. Geostationary satellites excel in monitoring specific regions with exceptional temporal persistence, while polar orbiters offer the broader perspective necessary to capture global climate trends and changes. The combination of these two satellite types complements each other's strengths and is instrumental in advancing our understanding of Earth's climate system!

A major hurdle in the realm of climate observation through satellites is their finite lifespan. On average, polar-orbiting satellites typically operate for approximately 5 to 10 years, while geostationary satellites often endure for roughly twice that duration. This poses a couple of significant challenges.

First, securing continuous financial resources – either from the governmental or private sectors -- becomes a critical imperative. Given the limited operational lifespan of satellites, ensuring sustainable funding for the ongoing replacement of these crucial instruments is essential. The maintenance of a robust satellite network hinges on the consistent investment required to effectively sustain climate monitoring endeavors and launch new and improved technology as warranted.

Secondly, the meticulous comparison of observations between different satellites demands careful attention. Satellite instruments are inherently diverse, and variations in calibration errors can exist between them. These discrepancies present challenges when striving to make accurate and seamless comparisons between observations collected by distinct generations of satellites. This is particularly vital when tracking ECVs. To understand the precision required in ECV data, satellite instruments must exhibit the capability to discern subtle atmospheric temperature trends, as minuscule as 0.10 degrees Celsius per decade. Likewise, they should possess the sensitivity to track ozone changes as minute as 1% per decade and variations in the sun's output as diminutive as 0.1% per decade. These exacting requirements underscore the importance of precise instrument calibration and meticulous data analysis to ensure the accuracy of climate observations over the long term.

An example of this “dance” is two recent NASA satellites used to measure precipitation: GPM (Global Precipitation Measurement), and TRMM (Tropical Rainfall Measuring Mission). TRMM was launched in 1997 and orbited in faithful service until 2015. When TRMM grew close to the end of its useful lifetime, GPM was funded by NASA and launched in 2014 with new instruments. Behind the scenes, the transition required an entire team solely dedicated to helping calibrate its observations so that they matched up with TRMM and that the observations from the two satellites could be glued together in time to teach us how precipitation has evolved over the past 30 years.

In addition to the satellites themselves, scientists develop data processing systems to “merge” multiple dataset sources together. We briefly touched on this above with in-situ observations, but naturally, you could easily imagine merging both multiple in-situ datasets with multiple remote sensing datasets! One such example in the U.S. is CMAP, which stands for the CPC Merged Analysis of Precipitation. CPC is an acronym for the Climate Prediction Center, the arm of the U.S.’s National Weather Service tasked with real-time monitoring of global climate and predictions of climate variability. After synthesis, such products allow us to create figures like the January mean precipitation climatology shown below. We arrive at a spatiotemporally continuous map of precipitation – “spatiotemporally continuous” means we can find a data point for any geographic location (spatio-) and any time (-temporal) as long as it’s contained in the dataset. Note the higher average precipitation totals in warm tropical regions, both over the ocean and in areas such as the Amazon rainforest, but some very dry areas in the subtropics such as the Saharan desert in Northern Africa. We’ll talk about the hydrological cycle and general circulation of the atmosphere later in the course to help explain why these patterns emerge.

Average January precipitation rate produced from CMAP data. Units are in mm/day.

Credit: CMAP: CPC Merged Analysis of Precipitation, National Center for Atmospheric Research (NCAR), is licensed under the Digital Millennium Copyright Act of 1998